Search

Objective: This paper presents a force control scheme for brief isotonic holds in an isometrically contracted muscle tissue, with minimal overshoot and settling time to measure its shortening velocity, a key parameter of muscle function. Methods: A two-degree-of-freedom control configuration, formed by a feedback controller and a feedforward controller, is explored. The feedback controller is a proportional-integral controller and the feedforward controller is designed using the inverse of a control-oriented model of muscle tissue. A generalized linear model and a nonlinear model of muscle tissue are explored using input-output data and system identification techniques. The force control scheme is tested on equine airway smooth muscle and its robustness confirmed with murine flexor digitorum brevis muscle. Results: Performance and repeatability of the force control scheme as well as the number of inputs and level of supervision required from the user were assessed with a series of experiments. The force control scheme was able to fulfill the stated control objectives in most cases, including the requirements for settling time and overshoot. Conclusion: The proposed control scheme is shown to enable automation of force control for characterizing muscle mechanics with minimal user input required. Significance: This paper leverages an inversion-based feedforward controller based on a nonlinear physiological model in a system identification context that is superior to classic linear system identification. The control scheme can be used as a steppingstone for generalized control of nonlinear, viscoelastic materials. [ABSTRACT FROM AUTHOR]

Wi-Fi fingerprint-based indoor localization has recently attracted significant research interest since Wi-Fi devices are widely deployed and practical, and no additional infrastructure is required. However, the Received Signal Strength (RSS) is significantly different on heterogeneous devices, and this difference has a negative impact on localization results. In this paper, we propose a multi-fingerprint and multi-classifier fusion (MFMCF) localization method to improve the localization accuracy and solve the problem of heterogeneous hardware. First, the individual feature set of original RSS fingerprint, signal strength difference (SSD) fingerprint and hyperbolic location fingerprint (HLF) are fused as a composite fingerprint set (CFS), and then the data dimension is reduced by linear discriminant analysis (LDA). Second, three representative machine learning algorithms including K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF) are selected and trained to construct an integrated fusion model. Finally, in order to get more accurate predictions, in the online phase, a selective strategy based on entropy is proposed by calculating the entropy of each classifier’s prediction result. Experiments show that MFMCF is an effective scheme to solve the localization problem of heterogeneous devices and improve the localization accuracy. [ABSTRACT FROM AUTHOR]

As an established biometric authentication approach, fingerprint scanning has received considerable attention due to its high accuracy and reliability. In this article, the fingerprint reconstruction at any position is achieved in large physical domains, which monitors wavefield variations of plate-like structures within arrays through the ultrasonic guided wave. Accurate reconstruction and quantitative characterization of fingerprints are obtained using fast inversion tomography (FIT) based on the deep learning convolutional neural network (DLCNN). Parametric optimization is conducted to reveal submillimeter fingerprint minutiae, and a specific DLCNN model is proposed for the artifact removal in FIT reconstructions. The results prove that the FIT based on DLCNN restoration can significantly improve the imaging quality in terms of increased resolution, reduced reconstruction errors, and higher fingerprint matching confidence. The reconstruction also allows an exponential improvement in computational efficiency as a result of much-reduced sensor numbers. Several factors affecting the performance of the proposed reconstruction method are discussed at the end. [ABSTRACT FROM AUTHOR]

Cancelable biometrics is a powerful remedy for information leakage caused by the extensive usage of unprotected biometric data. Current measures usually suffer from deteriorated accuracy, which is known as the security–performance tradeoff. Motivated by these concerns, in this article, a novel cancelable fingerprint approach, i.e., Indexing-Min–Max (IMM) hashing, is proposed to securely transform a fixed-length fingerprint feature vector to a discrete index hashed code. IMM hashing is essentially established upon the min–max hash and further strengthened by the integration of the partial Hadamard transform, which alleviates performance deterioration while maintaining a high security level. Extensive experiments on FVC2002 and FVC2004 fingerprint datasets coupled with comprehensive theoretical analyses demonstrate the favorable accuracy and strong anti-attack resilience of the proposed method. Besides, compared to the unprotected counterpart, the matching precision of the protected templates yields little accuracy loss or even improved performance, which means the security–performance tradeoff is well handled. Furthermore, IMM hashing also meets the unlinkability and revocability requisites of cancelable biometrics. [ABSTRACT FROM AUTHOR]

In many of the least developed and developing countries, a multitude of infants continue to suffer and die from vaccine-preventable diseases and malnutrition. Lamentably, the lack of official identification documentation makes it exceedingly difficult to track which infants have been vaccinated and which infants have received nutritional supplements. Answering these questions could prevent this infant suffering and premature death around the world. To that end, we propose Infant-Prints, an end-to-end, low-cost, infant fingerprint recognition system. Infant-Prints is comprised of our (i) custom built, compact, low-cost (85 USD), high-resolution (1,900 ppi), ergonomic fingerprint reader, and (ii) high-resolution infant fingerprint matcher. To evaluate the efficacy of Infant-Prints, we collected a longitudinal infant fingerprint database captured in four different sessions over a 12-month time span (December 2018 to January 2020), from 315 infants at the Saran Ashram Hospital, a charitable hospital in Dayalbagh, Agra, India. Our experimental results demonstrate, for the first time, that Infant-Prints can deliver accurate and reliable recognition (over time) of infants enrolled between the ages of 2-3 months, in time for effective delivery of vaccinations, healthcare, and nutritional supplements (TAR = 95.2% @ FAR = 1.0% for infants aged 8-16 weeks at enrollment and authenticated 3 months later). 1. A preliminary version of this paper was present at CVPRW Computer Vision for Global Challenges, Long Beach, CA, 2019. [ABSTRACT FROM AUTHOR]

Timely identification of the aging stage of the transformer can effectively ensure the safe operation of the transformer and prevent the aging fault of the transformer. This work proposes the aging diagnosis of transformer using the Raman spectroscopic recurrence plot combined with learning vector quantization (LVQ) neural network. Oil-paper insulation samples at different aging stages are obtained by accelerated thermal aging tests. Acquisition of Raman spectra of insulating oil is from 800 to 3000 cm−1 and converts to the Raman spectroscopic recurrence plot. The gray-level cooccurrence matrix (GLCM) is used to extract the characteristic information of the Raman spectroscopic recurrence plot. The optimal number of neurons in competitive layer of LVQ neural network is determined by the experimental method. A transformer aging diagnosis model based on the Raman spectroscopic recurrence plot is established. The results show that the diagnostic accuracy of the proposed diagnostic model reached 95%. The Raman spectroscopic recurrence plot has more abundant aging “fingerprint” characteristic information and the proposed diagnostic method shows potential for monitoring oil-paper insulation. [ABSTRACT FROM AUTHOR]

In recent years, biometric authentication technology for smartphones has become widespread, with the mainstream methods being fingerprint authentication and face recognition. However, fingerprint authentication cannot be used when hands are wet, and face recognition cannot be used when a person is wearing a mask. Like the face and fingers, the ear as a biometric contains features that enable human identification and has been the subject of research on personal authentication. Authentication systems based on the acoustic transfer function of the pinna (PRTF: Pinna Related Transfer Function) have been investigated. However, the authentication accuracy decreases due to the positional fluctuation across each measurement. In this paper, we propose multimodal personal authentication on smartphones using PRTF. The pinna image and positional sensor information are used with the PRTF, and the effectiveness of the authentication method is examined. Half total error rate (HTER) of 9.3% for single-modal authentication using only PRTF was improved to 1.6% for multimodal authentication using images and sensor data. We demonstrate that the proposed authentication system can compensate for the positional changes in each measurement and improve the robustness. [ABSTRACT FROM AUTHOR]

Human fingerprints are detailed and nearly unique markers of human identity. Such a unique and stable fingerprint is also left on each acquired image. It can reveal how an image was degraded during the image acquisition procedure and thus is closely related to the quality of an image. In this work, we propose a new no-reference image quality assessment (NR-IQA) approach called domain-aware IQA (DA-IQA), which for the first time introduces the concept of domain fingerprint to the NR-IQA field. The domain fingerprint of an image is learned from image collections of different degradations and then used as the unique characteristics to identify the degradation sources and assess the quality of the image. To this end, we design a new domain-aware architecture, which enables simultaneous determination of both the distortion sources and the quality of an image. With the distortion in an image better characterized, the image quality can be more accurately assessed, as verified by extensive experiments, which show that the proposed DA-IQA performs better than almost all the compared state-of-the-art NR-IQA methods. [ABSTRACT FROM AUTHOR]

Due to its low energy consumption and simplicity, near field communication (NFC) has been extensively used in various short-range transmission scenarios, for example, proximity payment and NFC entrance guard. However, the low data rate of NFC limits its application in high rate demanded scenarios, such as high-resolution fingerprint identification and streaming media transmission as well as the future promising high rate indoor communications among pads, phones, and laptops. In this paper, we model and analyze the performance of the orbital angular momentum based NFC (OAM-NFC) system, which can significantly increase the capacity of NFC. We first give the OAM system model. With coils circularly equipped at the transmitter and receiver, OAM-NFC signals can be transmitted, received, and detected. Then, we develop the OAM-NFC generation and detection schemes for NFC multiplexing transmission. We also analyze the OAM-NFC channel capacity and compare it with those of single-input-single-output (SISO) as well as multi-input-multi-output (MIMO) NFC. Simulation results validate the feasibility and capacity enhancement of our proposed OAM-NFC system. How different variables, such as the transceiver misalignment, the numbers of transceiver coils, and transceiver distance, impact the OAM-NFC capacity are also analyzed. [ABSTRACT FROM AUTHOR]

Fuzzy systems can aid in diminishing uncertainty and noise from biometric security applications by providing an intelligent layer to the existing physical systems to make them reliable. In the absence of such fuzzy systems, a little random perturbation in captured human biometrics could disrupt the whole security system, which may even decline the authentication requests of legitimate entities during the protocol execution. In the literature, few fuzzy logic-based biometric authentication schemes have been presented; however, they lack significant security features including perfect forward secrecy (PFS), untraceability, and resistance to known attacks. This article, therefore, proposes a novel two-factor biometric authentication protocol enabling efficient and secure combination of physically unclonable functions, a physical object analogous to human fingerprint, with user biometrics by employing fuzzy extractor-based procedures in the loop. This combination enables the participants in the protocol to achieve PFS. The security of the proposed scheme is tested using the well-known real-or-random model. The performance analysis signifies the fact that the proposed scheme not only offers PFS, untraceability, and anonymity to the participants, but is also resilient to known attacks using light-weight symmetric operations, which makes it an imperative advancement in the category of intelligent and reliable security solutions. [ABSTRACT FROM AUTHOR]

A large body of research has focused on analyzing large networks and graphs. However, network and graph data is often anonymized for reasons such as protecting data privacy. Under such circumstances, it is difficult to verify the source of network data, which leads to questions such as: Given an anonymized graph, can we identify the network from which it is collected? Or, if one claims the graph is sampled from a certain network, can we verify this claim? The intuitive approach is to check for subgraph isomophism. However, subgraph isomophism is NP-complete; hence, infeasible for most large networks. Inspired by biometrics studies, we address these challenges by formulating two new problems: network identification and network authentication. To tackle these problems, similar to research on human fingerprints, we introduce two versions of a network identity: (1) embedding-based identity and (2) distribution-based identity. We demonstrate the effectiveness of these network identities using extensive experiments on real-world networks. Using these identities, we propose two approaches for network identification. One method uses supervised learning and can achieve an identification accuracy of 84.4 percent, and the other, which is easier to implement, relies on distances between identities and achieves an accuracy rate of 70.8 percent. For network authentication, we propose two methods to build a network authentication system. The first is a supervised learner and yields a low false accept rate and the other method, allows one to control the false reject rate with a reasonable false accept rate across networks. We demonstrate that network authentication can also be used for biometrics, authenticating users based on their touch data on phones and tablets. Our study can help identify or verify the source of network data, validate network-based research, and be used for network-based biometrics. [ABSTRACT FROM AUTHOR]

Automated Fingerprint Recognition Systems (AFRSs) have been threatened by Presentation Attack (PA) since its existence. It is thus desirable to develop effective presentation attack detection (PAD) methods. However, the unpredictable PAs make PAD be a challenging problem. This paper proposes a novel One-Class PAD (OCPAD) method for Optical Coherence Technology (OCT) images based fingerprint PA detection. The proposed OCPAD model is learned from a training set only consists of Bonafides (i.e. real fingerprints). The reconstruction error and latent code obtained from the trained auto-encoder network in the proposed model is taken as the basis for the following spoofness score calculation. To get more accurate reconstruction error, we propose an activation map based weighting model to further refine the accuracy of reconstruction error. We test different statistics and distance measures and finally use a decision level fusion to make the final prediction. Our experiments are performed using a dataset with 93200 bonafide scans and 48400 PA scans. The results show that the proposed OCPAD can achieve a True Positive Rate (TPR) of 99.43% when the False Positive Rate (FPR) equals to 10% and a TPR of 96.59% when FPR=5%, which significantly outperformed a feature based approach and a supervised learning based model requiring PAs for training. [ABSTRACT FROM AUTHOR]

Contactless 3D finger knuckle is a new biometric identifier which can offer an accurate, efficient and convenient alternative for the personal identification. The current 3D finger knuckle recognition methods are limited by computationally complex or inefficient matching algorithms, which attempt to compute the matching scores from all possible translational and rotational parameters for matching a pair of templates. The strength of such approach lies in its simplicity and reliability for accurately matching intra-class samples, but expensive computational time is required. Furthermore, attempting on excessive numbers of translational and rotational parameters can also degrade the overall recognition accuracy because the imposter matches can be increased. In fact, this conventional matching approach is commonly adopted in many biometric studies, but its drawbacks have not received adequate attention. This article addresses such 3D finger knuckle recognition problem by developing a more efficient matching approach using surface key points extracted from 3D finger knuckle surfaces. Our comparative experimental results with the state-of-the art method on a publicly available 3D finger knuckle database indicates that our approach can offer over 23 times faster with performance improvement on the accuracy. Although the focus of our work is on 3D finger knuckle recognition, we also present the performance of our method on other publicly available databases with similar 3D biometric patterns including 3D palmprint and 3D fingerprint, to validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Deep learning methods have been very successful at radio frequency fingerprinting tasks, predicting the identity of transmitting devices with high accuracy. We study radio frequency fingerprinting deployments at resource-constrained edge devices. We use structured pruning to jointly train and sparsify neural networks tailored to edge hardware implementations. We compress convolutional layers by a $27.2\times$ 27. 2 × factor while incurring a negligible prediction accuracy decrease (less than 1 percent). We demonstrate the efficacy of our approach over multiple edge hardware platforms, including a Samsung Gallaxy S10 phone and a Xilinx-ZCU104 FPGA. Our method yields significant inference speedups, $11.5\times$ 11. 5 × on the FPGA and $3\times$ 3 × on the smartphone, as well as high efficiency: the FPGA processing time is $17\times$ 17 × smaller than in a V100 GPU. To the best of our knowledge, we are the first to explore the possibility of compressing networks for radio frequency fingerprinting; as such, our experiments can be seen as a means of characterizing the informational capacity associated with this specific learning task. [ABSTRACT FROM AUTHOR]

High-resolution fingerprint recognition has been a hot topic for many years. Compared with a traditional fingerprint image, a high-resolution fingerprint image can provide more features, such as pores and ridge contours. Introducing these features into fingerprint comparison and recognition can improve the recognition accuracy and reduce the risk of identification errors. This paper proposes a novel method for comparing pores on high-resolution fingerprint images. The method can be divided into two steps. In the first step, fingerprints are aligned using the pixel-category-distance-based data-driven descending algorithm. Traditionally, fingerprints are aligned based on feature points, such as minutiae and singular points. Such alignment methods are not suitable when dealing with partial fingerprints because small overlapping areas often do not contain enough features to guarantee a correct alignment. In this research, the ridges and valleys on fingerprints are used in combination with the orientation field for alignment. The proposed algorithm performs well when aligning both partial and full fingerprints. The common areas between the two images can be estimated based on the alignment result. In the second step, pores lying in the common areas are selected for comparison. To improve the comparison accuracy, pores are compared using local features and spatial relations. A graph comparison algorithm is designed in this step. The experimental results show that the proposed method is more accurate than other state-of-the-art pore comparison algorithms. [ABSTRACT FROM AUTHOR]

Fingerprint imaging is a powerful tool in biometric identification systems. This paper proposes a fingerprint imaging method that involves applying an impedance signal to a fingerprint via ultrasonic impediography using a capacitive micromachined ultrasonic transducer (CMUT). We fabricated CMUT sparse array which has a center frequency of 6.25 MHz and collapse voltage of 50 V by local oxidation of silicon process. We develop a prototype by using waveguide and ultrasonic impediography method based on the CMUT. By using a waveguide made from a hard material, the prototype can ensure device protection and image lateral resolution improvement. The proposed technique successfully images the acoustic input impedance differences between the ridges and valleys of a fingerprint. The images created using direct touch and waveguide models are compared and analyzed. In addition, we present a phenomenon in the waveguide system in terms of energy loss by using numerical simulation. Therefore, this study facilitates ultrasonic fingerprint impediography measurement based on the basic direct touch and quartz glass waveguide system. [ABSTRACT FROM AUTHOR]

A highly accurate 3-D indoor passive localization and identification system is presented in this article. The system can monitor various commercial Wi-Fi devices through the proposed received signal strength indicator (RSSI)-based angle of arrival (AoA) estimation technique. RSSI-based localization systems conventionally use additional assistance techniques, such as distance–RSSI calibration, fingerprint analysis, machine learning, and a widespread setup to achieve high accuracy. On the contrary, this proposed system can operate in complex environments full of scattering objects and obstructions without requiring any additional assistance techniques. The proposed technique uses a six-port network to evaluate the phase difference in the carrier waves of Wi-Fi signals without the influence of modulated signals. The network also preserves the modulated signals without the influence of the phase difference of the carrier waves such that Wi-Fi devices can be identified. Regarding practical applications, the system is designed to be capable of detecting devices through walls and thus can be hidden outside the monitored room. The experimental results indicate that in single-source localization, the average error is 0.089 m; in multiple-source localization, it is 0.354 m; and for seeing through the wall, it is 0.24 m. In the worst case scenario, the error is still smaller than 0.63 m. The accuracy in all the experimental results was found to be at the decimeter level. In summary, this study experimentally validated a 3-D indoor localization and identification system for diverse Wi-Fi devices in various environments. [ABSTRACT FROM AUTHOR]

Acoustic fingerprinting aims to identify a mobile device based on its internal microphone(s) and speaker(s) which are unique due to manufacturing imperfection. This paper seeks a thorough understanding of the (in)security of exploring acoustic fingerprints for achieving distributed mobile authentication. Our contributions are threefold. First, we present a new acoustic fingerprint-emulation attack and demonstrate that it is a common vulnerability of acoustic mobile authentication systems. Second, we propose a dynamic challenge-response defense to secure acoustic mobile authentication systems against the acoustic fingerprint-emulation attack. Finally, we thoroughly investigate existing acoustic fingerprinting schemes and identify the best option for accurate, secure, and deployable acoustic mobile authentication systems. [ABSTRACT FROM AUTHOR]

Audio fingerprinting methods can compress audio contents into compact signatures so that we can save storage and reduce query time. This technology is widely used in many fields, such as audio retrieval, music information retrieval and audio authentication. However, most of the existing methods cannot balance the recognition accuracy, query speed and storage size well. This letter presents a novel self-supervised learning scheme called asymmetric contrastive learning to generate binary hash fingerprints of audio segments. Meanwhile, we design a new loss function named bidirectional asymmetric pairwise loss to minimize the loss of information. Experimental results show that our scheme can achieve a high top-1 hit rate on both music and speech datasets. Furthermore, the proposed scheme outperforms the previous work of real-value fingerprinting in query speed and storage size. [ABSTRACT FROM AUTHOR]

This paper proposes a sound separation methodology based on deep learning neural network (DLNN) to extract useful diagnostic material in non-invasive audio-based On-Load Tap Changer (OLTC) diagnostics. The proposed methodology has been experimentally verified on both artificial mixtures (created by reproducing the targeted data by the speakers placed next to the active transformers) and actual mixtures (recorded by the microphone during OLTC live operation in the field). The results show that the method produces high-quality estimates (correlation to referent fingerprints $\rho > 0.9$) compared to other sound separation methods, e.g. Independent Component Analysis (ICA). The proposed framework can also perform source separation from a monaural mixture (mixture recorded with a single microphone only), which is impossible for ICA methods. Moreover, the results show that DLNN trained with healthy OLTC data produces diagnostically valuable estimates even when fed with a faulty OLTC audio mixture. For that reason, once trained, the DLNN can produce the diagnostic signal estimates from monaural mixtures that can be used with existing vibration-based diagnostic methods. [ABSTRACT FROM AUTHOR]

Due to the premise of uniqueness and acceptance, fingerprint has been the most adopted biometric technologies in high-impact applications (e.g., smartphone security, monetary transactions and international-border verification). Although there are an array of commercial fingerprint scanners across different sensing modalities including optical, capacitive, thermal and ultrasonic, existing fingerprint technologies are vulnerable to spoofing attacks via fake-finger in Kang et al., 2003. In this paper, we investigate a new dimension of fingerprint sensing based on the friction-excited sonic wave (in simpler words, ”voice of fingerprint”) from a user swiping his fingertip on everyday surfaces. Specifically, we develop SonicPrint to leverage the intrinsic fingerprint ridge information in sonic wave for user identification. First, the complex ambient noise is isolated from the sonic wave using background isolation and adaptive segmentation models. Afterward, a series of multi-level friction descriptors that highlight the target fingerprint information is extracted. These descriptors are fed to a specially designed ensemble classifier for user identification. SonicPrint is practical as it leverages in-built microphones in smart devices, requiring no hardware modifications. As the first exploratory study, our experimental results with 31 participants over three different swipe actions on 12 different types of materials show up to a 98 percent identification accuracy. [ABSTRACT FROM AUTHOR]

Emerging 3D fingerprint recognition technologies have attracted growing attention in addressing the limitations from contact-based fingerprint acquisition and improve recognition accuracy. However, the complex 3D imaging setups employed in these systems typically require structured lighting with scanners or multiple cameras which are bulky with higher cost. This paper presents a more accurate and efficient 3D fingerprint identification approach using a single 2D camera with multiple colored LED illumination. A 3D minutiae tetrahedron based algorithm is developed to more efficiently match recovered minutiae features in 3D space and address the limitations of 3D minutiae matching approach in the literature. This algorithm significantly improves the matching time to about 15 times than the state-of-art in the reference. A hierarchical tetrahedron matching scheme is also developed to further improve the matching accuracy with faster speed. The 2D images acquired to reconstruct the 3D fingerprints are also used to recover 2D minutiae and further improve matching performance for 3D fingerprints. A new two-session database acquiring from 300 different clients consists of 2760 3D fingerprints reconstructed from 5520 colored 2D fingerprints is also developed and shared in public domain to further advance much needed research in this area. Extensive experimental results presented in this paper validate our approach and demonstrate the effectiveness of proposed algorithms. [ABSTRACT FROM AUTHOR]

WiFi-Fingerprint is extensively utilized for indoor localization with the advent of the high-density wireless networks deployment and research on ubiquitous intelligence. Nevertheless, establishing an elaborate radio map for localization is a highly time-consuming task. Aiming to alleviate this problem and enhance WiFi-based indoor localization accuracy, this paper has done the following contributions. First, we present Gaussian process regression models to predict the spatial distribution of signal strength in the uncalibrated domain with limited known labeled fingerprints in reference points. As a result, deployment effort for radio mapping can be greatly reduced. Second, in order to acquire fingerprints data with higher accuracy, the compound kernels for received signal strength (RSS) prediction models are presented. Finally, the definite position is determined with the weighted Similarity K-Nearest Neighbor localization algorithm when new observation RSS is collected. The experiments show that compared with the original reference fingerprints localization system, the proposed localization system explicitly reduces the localization error. The results further demonstrate that our method can augment the fingerprints and improve the accuracy of fingerprint-based indoor localization without extra manual calibration or adding dedicated infrastructure. [ABSTRACT FROM AUTHOR]

Fingerprinting localization systems are outstanding for its convenient deployment, where a major challenge is the high cost for collecting a huge number of received signal strength fingerprints. Mobile crowdsensing (MCS) paradigm is cost-effective for large-scale data collection; however, a quality-aware data pricing mechanism dedicated to MCSed fingerprints accommodating practical application situations including budget constraints and online data submission is still unavailable. In this paper, we present a data pricing scheme dedicated to MCSed fingerprints by enhancing the online learning technique. We first reveal the principle of fingerprints quality assessment for accurate localization. Based on the principle, we design corresponding loss and regret function, which is able to reflect values of the fingerprints with respect to localization accuracy. We then present an online pricing scheme for MCSed data, which results in that the worker's payoff is a random variable following an optimal probability density function leading to the minimum expected regret. Furthermore, we extend our scheme to application scenarios with different budget settings, where the pricing strategies for the scenarios of regret minimization with fixed budget and budget minimization for certain fingerprints quality level are investigated. Experimental results are presented to verify our theoretical analysis. [ABSTRACT FROM AUTHOR]

Facial attributes in StyleGAN generated images are entangled in the latent space which makes it very difficult to independently control a specific attribute without affecting the others. Supervised attribute editing requires annotated training data which is difficult to obtain and limits the editable attributes to those with labels. Therefore, unsupervised attribute editing in an disentangled latent space is key to performing neat and versatile semantic face editing. In this paper, we present a new technique termed Structure-Texture Independent Architecture with Weight Decomposition and Orthogonal Regularization (STIA-WO) to disentangle the latent space for unsupervised semantic face editing. By applying STIA-WO to GAN, we have developed a StyleGAN termed STGAN-WO which performs weight decomposition through utilizing the style vector to construct a fully controllable weight matrix to regulate image synthesis, and employs orthogonal regularization to ensure each entry of the style vector only controls one independent feature matrix. To further disentangle the facial attributes, STGAN-WO introduces a structure-texture independent architecture which utilizes two independently and identically distributed (i.i.d.) latent vectors to control the synthesis of the texture and structure components in a disentangled way. Unsupervised semantic editing is achieved by moving the latent code in the coarse layers along its orthogonal directions to change texture related attributes or changing the latent code in the fine layers to manipulate structure related ones. We present experimental results which show that our new STGAN-WO can achieve better attribute editing than state of the art methods. [ABSTRACT FROM AUTHOR]

Lots of Deepfake videos are circulating on the Internet, which not only damages the personal rights of the forged individual, but also pollutes the web environment. What’s worse, it may trigger public opinion and endanger national security. Therefore, it is urgent to fight deep forgery. Most of the current forgery detection algorithms are based on convolutional neural networks to learn the feature differences between forged and real frames from big data. In this paper, from the perspective of image generation, we simulate the forgery process based on image generation and explore possible trace of forgery. We propose a multi-scale self-texture attention Generative Network(MSTA-Net) to track the potential texture trace in image generation process and eliminate the interference of deep forgery post-processing. Firstly, a generator with encoder-decoder is to disassemble images and performed trace generation, then we merge the generated trace image and the original map, which is input into the classifier with Resnet as the backbone. Secondly, the self-texture attention mechanism(STA) is proposed as the skip connection between the encoder and the decoder, which significantly enhances the texture characteristics in the image disassembly process and assists the generation of texture trace. Finally, we propose a loss function called Prob-tuple loss restricted by classification probability to amend the generation of forgery trace directly. To verify the performance of the MSTA-Net, we design different experiments to verify the feasibility and advancement of the method. Experimental results show that the proposed method performs well on deep forged databases represented by FaceForensics++, Celeb-DF, Deeperforensics and DFDC, and some results are reaching the state-of-the-art. [ABSTRACT FROM AUTHOR]

Conventional biometric modalities, such as the face, fingerprint, and iris, are vulnerable against imitation and circumvention. Accordingly, secure biometric modalities with cancelable properties are needed for personal identification, especially in smart healthcare applications. Here we developed a person identification model using high-density surface electromyography (HD-sEMG) as biometric traits. In this model, the HD-sEMG biometric templates are cancelable and could be customized by the users through finger isometric contractions. A deep feature learning approach, implemented by convolutional neural networks (CNNs) is used to capture user-specific patterns from HD-sEMG signals and make identification decisions. This model has been validated on twenty-two subjects, with training and testing data acquired from two different days. The rank-1 identification accuracy and equal error rate for 44 identities (22 subjects × 2 accounts) can reach 87.23% and 4.66%, respectively. The cross-day identification accuracy of the proposed model is higher than the results of previous methods reported in the literature. The usability and efficiency of the proposed model are also investigated, indicating its potentials for practical applications. [ABSTRACT FROM AUTHOR]

Most existing fingerprints-based indoor localization approaches are based on some single fingerprint, such as received signal strength (RSS), channel impulse response, and signal subspace. However, the localization accuracy obtained by the single fingerprint approach is rather susceptible to the changing environment, multipath, and non-line-of-sight propagation. In this paper, we propose a novel localization framework by Fusing A Group Of fingerprinTs (FAGOT) via multiple antennas for the indoor environment. We first build a GrOup Of Fingerprints (GOOF), which includes five different fingerprints, namely, RSS, covariance matrix, signal subspace, fractional low-order moment, and fourth-order cumulant, which are obtained by different transformations of the received signals from multiple antennas in the offline stage. Then, we design a parallel GOOF multiple classifiers based on AdaBoost (GOOF-AdaBoost) to train each of these fingerprints in parallel as five strong multiple classifiers. In the online stage, we input the corresponding transformations of the real measurements into these strong classifiers to obtain independent decisions. Finally, we propose an efficient combination fusion algorithm, namely, MUltiple Classifiers mUltiple Samples (MUCUS) fusion algorithm to improve the accuracy of localization by combining the predictions of multiple classifiers with different samples. As compared with the single fingerprint approaches, our proposed approach can improve the accuracy and robustness of localization significantly. We demonstrate the feasibility and performance of the proposed algorithm through extensive simulations as well as via real experimental data using a Universal Software Radio Peripheral platform with four antennas. [ABSTRACT FROM PUBLISHER]

A real-time image filtering technique is proposed which could result in faster implementation for fingerprint image enhancement. One major hurdle associated with fingerprint filtering techniques is the expensive nature of their hardware implementations. To circumvent this, a modified anisotropic Gaussian filter is efficiently adopted in hardware by decomposing the filter into two orthogonal Gaussians and an oriented line Gaussian. An architecture is developed for dynamically controlling the orientation of the line Gaussian filter. To further improve the performance of the filter, the input image is homogenized by a local image normalization. In the proposed structure, for a middle-range reconfigurable FPGA, both parallel compute-intensive and real-time demands were achieved. We manage to efficiently speed up the image-processing time and improve the resource utilization of the FPGA. Test results show an improved speed for its hardware architecture while maintaining reasonable enhancement benchmarks. [ABSTRACT FROM PUBLISHER]

Despite new technologies that make face detection and recognition more sophisticated, long-recognized problems in security, privacy, and accuracy persist. Refining this technology and introducing it into new domains will require solving these problems through focused interdisciplinary efforts among developers, researchers, and policymakers. [ABSTRACT FROM PUBLISHER]

The ISO/IEC 19794–2-compliant fingerprint minutiae template is an unordered and variable-sized point set data. Such a characteristic leads to a restriction for the applications that can only operate on fixed-length binary data, such as cryptographic applications and certain biometric cryptosystems (e.g., fuzzy commitment). In this paper, we propose a generic point-to-string conversion framework for fingerprint minutia based on kernel learning methods to generate discriminative fixed length binary strings, which enables rapid matching. The proposed framework consists of four stages: 1) minutiae descriptor extraction; 2) a kernel transformation method that is composed of kernel principal component analysis or kernelized locality-sensitive hashing for fixed length vector generation; 3) a dynamic feature binarization; and 4) matching. The promising experimental results on six datasets from fingerprint verification competition (FVC)2002 and FVC2004 justify the feasibility of the proposed framework in terms of matching accuracy, efficiency, and template randomness. [ABSTRACT FROM PUBLISHER]

RF fingerprinting exploits the variations in the RF chain of radios to uniquely identify transmitters, and distinguish adversarial transmissions from the transmissions of legitimate nodes. We provide a systematic approach rooted from the information theory to evaluate the basic performance limits of RF fingerprinting. We develop a novel channel model for RF fingerprinting, where the imperfections in the RF chain are modeled as a fingerprint channel, cascaded to the actual physical channel. We address the authentication problem in the presence of an adversary, where both the legitimate transmitter and the adversary are equipped with unique fingerprint channels, in addition to a possible secret key available at the legitimate nodes. We provide bounds for the error exponents for reliable communication of the legitimate nodes, and the success exponent for impersonation and substitution attacks of the adversary, as a function of certain parameters based on their RF-fingerprints, and the shared key rate. We illustrate that keyless authentication is possible via RF fingerprints when the legitimate channel is not simulatable. We also show that the probability of these attacks can be reduced significantly by employing additional dedicated authenticated nodes. [ABSTRACT FROM PUBLISHER]

Location service is one of the primary services in smart automated systems of Internet of Things (IoT). For various location-based services, accurate localization has become a key issue. Recently, research on IoT localization systems for smart buildings has been attracting increasing attention. In this paper, we propose a novel localization approach that utilizes the neighbor relative received signal strength to build the fingerprint database and adopts a Markov-chain prediction model to assist positioning. The approach is called the novel localization method (LNM) in short. In the proposed LNM scheme, the history data of the pedestrian’s locations are analyzed to further lower the unpredictable signal fluctuations in a smart building environment, meanwhile enabling calibration-free positioning for various devices. The performance evaluation conducted in a realistic environment shows that the presented method demonstrates superior localization performance compared with well-known existing schemes, especially when the problems of device heterogeneity and WiFi signals fluctuation exist. [ABSTRACT FROM PUBLISHER]

Of the different branches of indoor localization research, WiFi fingerprinting has drawn significant attention over the past decade. These localization systems function by comparing WiFi received signal strength indicator (RSSI) and a pre-established location-specific fingerprint map. However, due to the time-variant wireless signal strength, the RSSI fingerprint map needs to be calibrated periodically, incurring high labor and time costs. In addition, biased RSSI measurements across devices along with transmission power control techniques of WiFi routers further undermine the fidelity of existing fingerprint-based localization systems. To remedy these problems, we propose GradIent FingerprinTing (GIFT) which leverages a more stable RSSI gradient. GIFT first builds a gradient-based fingerprint map (Gmap) by comparing absolute RSSI values at nearby positions, and then runs an online extended particle filter (EPF) to localize the user/device. By incorporating Gmap, GIFT is more adaptive to the time-variant RSSI in indoor environments, thus effectively reducing the overhead of fingerprint map calibration. We implemented GIFT on Android smartphones and tablets, and conducted extensive experiments in a five-story campus building. GIFT is shown to achieve an 80 percentile accuracy of 5.6 m with dynamic WiFi signals. [ABSTRACT FROM AUTHOR]

Touchless fingerprint recognition systems do not require contact of the finger with any acquisition surface and thus provide an increased level of hygiene, usability, and user acceptability of fingerprint-based biometric technologies. The most accurate touchless approaches compute 3-D models of the fingertip. However, a relevant drawback of these systems is that they usually require constrained and highly cooperative acquisition methods. We present a novel, fully touchless fingerprint recognition system based on the computation of 3-D models. It adopts an innovative and less-constrained acquisition setup compared with other previously reported 3-D systems, does not require contact with any surface or a finger placement guide, and simultaneously captures multiple images while the finger is moving. To compensate for possible differences in finger placement, we propose novel algorithms for computing 3-D models of the shape of a finger. Moreover, we present a new matching strategy based on the computation of multiple touch-compatible images. We evaluated different aspects of the biometric system: acceptability, usability, recognition performance, robustness to environmental conditions and finger misplacements, and compatibility and interoperability with touch-based technologies. The proposed system proved to be more acceptable and usable than touch-based techniques. Moreover, the system displayed satisfactory accuracy, achieving an equal error rate of 0.06% on a dataset of 2368 samples acquired in a single session and 0.22% on a dataset of 2368 samples acquired over the course of one year. The system was also robust to environmental conditions and to a wide range of finger rotations. The compatibility and interoperability with touch-based technologies was greater or comparable to those reported in public tests using commercial touchless devices. [ABSTRACT FROM PUBLISHER]

Electrocardiogram (ECG) biometric recognition as a personal identification method is receiving more and more attention because it can support live verification results. Compared with other biometric-based methods, it can provide higher security performance. The difficulty of the problem lies in how to stably extract ECG signal features and achieve real-time verification. In this study, a new type of sparse representation learning framework called statistical $n$ -best adaptive Fourier decomposition (SAFD) originated by Qian is adopted in ECG biometric identification. Adaptive Fourier decomposition (AFD) is a recently developed combination of transform-based signal decomposition and sparse representation method, which can adaptively select the atoms from a redundant dictionary through orthogonal processing. The advantage of the AFD-type methods is that each atom in the dictionary has a precise mathematical formula with good analytic properties. This characteristic is significantly distinguished it from other existing sparse representations, where the atoms learned are usually matrix data and cannot be described mathematically. The proposed SAFD extends the existing $n$ -best AFD from processing single signal to multi-signals and implements the $n$ -best AFD in the stochastic Hardy space. Therefore, the small number of learned atoms by SAFD is sufficient to capture internal structure and robustness of the signal and generate a discriminative representation that reflects the time–frequency characteristics of signals. It is very suitable for non-stationary signals like ECG. The proof of convergence of the algorithm is presented. Extensive experiments are conducted on five public databases collected in different realistic conditions, and an average identification accuracy of 98.0% is achieved. In addition, less than 1 ms for one matching process makes it possible to be implemented in real time. Experimental results demonstrate that the proposed method can achieve superior performance compared to other state-of-the-art ECG biometric identification methods. [ABSTRACT FROM AUTHOR]

In reverse engineering, the smooth results of the subtle features of the point cloud data will directly affect the precision of machining quality for large and complex curved workpieces. However, many widely used point cloud filters are not as effective as expected for special surface models. This article proposes the feature smoothing algorithm based on a multichannel convolutional neural network (CNN). Specifically, our approach consists of three stages. First, feature extraction operations are performed on the point cloud data, and the feature point set is marked. Second, the input samples of CNN are constructed according to the feature point set and the nonfeature point set. Third, based on the CNN, a locally smooth surface is fitted near the feature point, and the smooth position of the feature point is predicted. Finally, the proposed algorithm is simulated and analyzed with the actual point cloud data of large spherical crown workpieces. The experimental results show that the proposed algorithm has better feature smoothing accuracy and eliminates the data offset caused by other filtering algorithms in the process of surface feature smoothing. [ABSTRACT FROM AUTHOR]

The digital reconstruction of neurons is essential to various neuroscientific studies. Due to the existence of gaps and ambiguities in neuron images, the neuron tracing results generated by most automatic reconstruction algorithms may be incomplete, resulting in false negatives (FNs), which need to be repaired in proof editing. However, the automatic proof-editing methods for repairing FN branches have rarely been explored. In this study, we propose a proof-editing algorithm for automatically detecting and repairing the FN branches of the initial reconstruction, which is based on a multiscale upgraded ray (MUR)-shooting model and an MOST-based repairer. The MUR detects the FN branch and the corresponding branch direction vector by analyzing the multiscale intensity distribution features around a topological feature point. The topological feature points contain the junction points detected from the neuron image and the tip nodes extracted from the initial reconstruction. The MOST-based repairer is proposed to prevent the redundant reconstructions by assigning the detected branch direction vector as the initial tracing direction, which rejects the nodes returning to the traced area. The experimental results demonstrate clearly that the proposed method can reduce 20% of the false-negative rate at most. The experimental results confirm that the proposed method is extremely helpful for generating faithful reconstructions. [ABSTRACT FROM AUTHOR]

Welding quality detection is a critical link in modern manufacturing, and the weld bead location is a prerequisite for the high-precision assessment of welding quality. It is generally necessary for weld bead detection to be accomplished in the context of complex industrial environments. However, conventional detection and location methods based on specific detection conditions or prior knowledge lack accuracy and adaptability. To precisely detect and locate the weld beads in real industrial environments, a novel weld bead detection and location algorithm is proposed based on deep convolutional neural networks. Because there is no open data set of weld beads and the samples in real industrial applications are insufficient for effective model training of the deep convolutional neural network, a novel data augmentation method based on a deep semantic segmentation network is proposed to increase the sample diversity and enlarge the data set. Then, a dynamic sample updating strategy is put forward to cover more welding situations. Finally, faced with the weak-feature and weak-texture characteristics of weld beads, a simplified YOLOV3 model is proposed to realize end-to-end weld bead location. Experiments demonstrate that the proposed method could effectively satisfy the robustness and precision requirements for weld bead detection and location combined with a deep semantic segmentation network and simplified YOLOV3 model. [ABSTRACT FROM AUTHOR]

Visible light positioning (VLP) is a promising indoor localization method as it provides high positioning accuracy and allows for leveraging the existing lighting infrastructure. Photodiode (PD)-based receiver is a commonly used tag for VLP. However, a tag employing a single PD requires three or more luminaires to be visible. This article presents a VLP system that uses a custom-made tag utilizing multiple PDs. It applies received signal strength (RSS)-based fingerprinting using a weighted k-nearest neighbor (WkNN) algorithm for localization. Experimental results show that it is possible to localize using less than three luminaires with high accuracy. The Manhattan and Matusita distance metrics are found to provide lower localization accuracy than the Euclidean metric for the WkNN algorithm. The creation of a dense fingerprinting database through 2-D interpolation is presented as a method to reduce the cost of time and labor. The localization performance of the VLP system does not degrade noticeably with the fabricated database. The localization accuracy of the WkNN algorithm is shown to be better than that of a multilayer perceptron (MLP)-based regressor. The developed VLP system is also experimentally benchmarked against the HTC Vive showing comparable performance. [ABSTRACT FROM AUTHOR]

Muscle synergy analysis is a useful tool for the evaluation of the motor control strategies and for the quantification of motor performance. Among the parameters that can be extracted, most of the information is included in the rank of the modular control model (i.e. the number of muscle synergies that can be used to describe the overall muscle coordination). Even though different criteria have been proposed in literature, an objective criterion for the model order selection is needed to improve reliability and repeatability of MSA results. In this paper, we propose an Akaike Information Criterion (AIC)-based method for model order selection when extracting muscle synergies via the original Gaussian Non-Negative Matrix Factorization algorithm. The traditional AIC definition has been modified based on a correction of the likelihood term, which includes signal dependent noise on the neural commands, and a Discrete Wavelet decomposition method for the proper estimation of the number of degrees of freedom of the model, reduced on a synergy-by-synergy and event-by-event basis. We tested the performance of our method in comparison with the most widespread ones, proving that our criterion is able to yield good and stable performance in selecting the correct model order in simulated EMG data. We further evaluated the performance of our AIC-based technique on two distinct experimental datasets confirming the results obtained with the synthetic signals, with performances that are stable and independent from the nature of the analysed task, from the signal quality and from the subjective EMG pre-processing steps. [ABSTRACT FROM AUTHOR]

Elastic distortion of fingerprints is one of the major causes for false non-match. While this problem affects all fingerprintrecognition applications, it is especially dangerous in negative recognition applications, such as watchlist and deduplicationapplications. In such applications, malicious users may purposely distort their fingerprints to evade identification. In this paper, weproposed novel algorithms to detect and rectify skin distortion based on a single fingerprint image. Distortion detection is viewed as a two-class classification problem, for which the registered ridge orientation map and period map of a fingerprint are used as the feature vector and a SVM classifier is trained to perform the classification task. Distortion rectification (or equivalently distortion field estimation) is viewed as a regression problem, where the input is a distorted fingerprint and the output is the distortion field. To solve this problem, a database (called reference database) of various distorted reference fingerprints and corresponding distortion fields is built in the offline stage, and then in the online stage, the nearest neighbor of the input fingerprint is found in the reference database and the corresponding distortion field is used to transform the input fingerprint into a normal one. Promising results have been obtained on three databases containing many distorted fingerprints, namely FVC2004 DB1, Tsinghua Distorted Fingerprint database, and the NIST SD27 latent fingerprint database. [ABSTRACT FROM PUBLISHER]

Human identification using fingerprint impressions has been widely studied and employed for more than 2000 years. Despite new advancements in the 3D imaging technologies, widely accepted representation of 3D fingerprint features and matching methodology is yet to emerge. This paper investigates 3D representation of widely employed 2D minutiae features by recovering and incorporating (i) minutiae height z and (ii) its 3D orientation φ information and illustrates an effective matching strategy for matching popular minutiae features extended in 3D space. One of the obstacles of the emerging 3D fingerprint identification systems to replace the conventional 2D fingerprint system lies in their bulk and high cost, which is mainly contributed from the usage of structured lighting system or multiple cameras. This paper attempts to addresses such key limitations of the current 3D fingerprint technologies bydeveloping the single camera-based 3D fingerprint identification system. We develop a generalized 3D minutiae matching model and recover extended 3D fingerprint features from the reconstructed 3D fingerprints. 2D fingerprint images acquired for the 3D fingerprint reconstruction can themselves be employed for the performance improvement and have been illustrated in the work detailed in this paper. This paper also attempts to answer one of the most fundamental questions on the availability of inherent discriminableinformation from 3D fingerprints. The experimental results are presented on a database of 240 clients 3D fingerprints, which is made publicly available to further research efforts in this area, and illustrate the discriminant power of 3D minutiae representation andmatching to achieve performance improvement. [ABSTRACT FROM PUBLISHER]

The research of external fingerprint collected by total internal reflection (TIR) has been carried out for decades and the research of internal fingerprint collected by optical coherence tomography (OCT) has just begun. The internal fingerprint can be hardly affected by the finger surface status, due to its strong antiinterference and antispoofing ability, which can serve as a powerful supplement to external fingerprint. However, matching fingerprints acquired in different ways can lead to a drop in fingerprint recognition accuracy due to the differences in fingerprint quality, distortions, and detection areas. Whether the internal fingerprint can be used to replace the external fingerprint for direct identification has been hampered by lacking comparison tools to study potential correlation with each other. To study the connection between internal and external fingerprints, a synchronous acquisition system that achieves the same fingerprint area measurement at the same time is necessary. The integration of different optical paths of TIR and OCT as well as the consequent distortion correction of two totally different imaging ways are two challenging problems. This article presents a fingerprint acquisition system that synchronously acquires the external fingerprint and internal fingerprint with TIR and OCT, respectively. By using a self-designed trapezoidal prism, optical paths of TIR and OCT were integrated. Furthermore, a distortion correction method based on a grid calibration plate is used to remove distortions caused by two imaging methods. The fingerprint quality statistics are illustrated in 264 sets of fingerprint data sets. Identification results show synchronous acquisition and distortion correction of the proposed system are efficient. [ABSTRACT FROM AUTHOR]

Recent research has established the possibility of deducing soft-biometric attributes such as age, gender, and race from an individual’s face image with high accuracy. However, this raises privacy concerns, especially when face images collected for biometric recognition purposes are used for attribute analysis without the person’s consent. To address this problem, we develop a technique for imparting soft biometric privacy to face images via an image perturbation methodology. The image perturbation is undertaken using a GAN-based Semi-Adversarial Network (SAN) — referred to as PrivacyNet — that modifies an input face image such that it can be used by a face matcher for matching purposes but cannot be reliably used by an attribute classifier. Further, PrivacyNet allows a person to choose specific attributes that have to be obfuscated in the input face images (e.g., age and race), while allowing for other types of attributes to be extracted (e.g., gender). Extensive experiments using multiple face matchers, multiple age/gender/race classifiers, and multiple face datasets demonstrate the generalizability of the proposed multi-attribute privacy enhancing method across multiple face and attribute classifiers. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) have been extensively applied in image processing, including visual saliency map prediction of images. A major difficulty in using a DNN for visual saliency prediction is the lack of labeled ground truth of visual saliency. A powerful DNN usually contains a large number of trainable parameters. This condition can easily lead to model over-fitting. In this study, we develop a novel method that overcomes such difficulty by embedding hierarchical knowledge of existing visual saliency models in a DNN. We achieve the objective of exploiting the knowledge contained in the existing visual saliency models by using saliency maps generated by local, global, and semantic models to tune and fix about 92.5% of the parameters in our network in a hierarchical manner. As a result, the number of trainable parameters that need to be tuned by the ground truth is considerably reduced. This reduction enables us to fully utilize the power of a large DNN and overcome the issue of over-fitting at the same time. Furthermore, we introduce a simple but very effective center prior in designing the learning cost function of the DNN by attaching high importance to the errors around the image center. We also present extensive experimental results on four commonly used public databases to demonstrate the superiority of the proposed method over classical and state-of-the-art methods on various evaluation metrics. [ABSTRACT FROM AUTHOR]

Face sketch synthesis from a photo is of significant importance in digital entertainment. An intelligent face sketch synthesis system requires a strong robustness to lighting variations. Under uncontrolled lighting conditions in real-world settings, such a system will perform consistently well and have little restriction on the lighting conditions. However, previous face sketch synthesis methods tend to synthesize sketches under well-controlled lighting conditions. These methods are sensitive to lighting variations and produce unsatisfactory results when the lighting condition varies. In this paper, we propose a novel cascaded face sketch synthesis framework composed of a multiple feature generator and a cascaded low-rank representation. The multiple feature generator not only produces a generated sketch feature consistent with an artist’s drawing style but also extracts a photo feature that is robust to various illuminations. Both features ensure that given a photo patch, the optimal sketch candidates can be selected from the database. The cascaded low-rank representation enables a gradual reduction in the gap between the synthesized face sketch and the corresponding artist-drawn sketch. Experimental results illustrate that the proposed cascaded framework generates realistic sketches on par with the current methods on the Chinese University of Hong Kong face sketch database under well-controlled illuminations. Moreover, this framework exhibits greatly improved performance compared to these methods on the extended Chinese University of Hong Kong face sketch database and Chinese celebrity face photos from the web under different illuminations. We argue that this framework paves a novel way for the implementation of computer-aided optical systems that are of essential importance in both face sketch synthesis and optical imaging. [ABSTRACT FROM AUTHOR]

Latent fingerprints serve as an important source of forensic evidence in a court of law. Automatic matching of latent fingerprints to rolled/plain (exemplar) fingerprints with high accuracy is quite vital for such applications. However, latent impressions are typically of poor quality with complex background noise which makes feature extraction and matching of latents a significantly challenging problem. We propose incorporating top-down information or feedback from an exemplar to refine the features extracted from a latent for improving latent matching accuracy. The refined latent features (e.g. ridge orientation and frequency), after feedback, are used to re-match the latent to the top $K$ candidate exemplars returned by the baseline matcher and resort the candidate list. The contributions of this research include: (i) devising systemic ways to use information in exemplars for latent feature refinement, (ii) developing a feedback paradigm which can be wrapped around any latent matcher for improving its matching performance, and (iii) determining when feedback is actually necessary to improve latent matching accuracy. Experimental results show that integrating the proposed feedback paradigm with a state-of-the-art latent matcher improves its identification accuracy by 0.5-3.5 percent for NIST SD27 and WVU latent databases against a background database of 100k exemplars. [ABSTRACT FROM AUTHOR]

Access point (APs) selection approaches have been used in location fingerprinting systems to improve positioning accuracy and to reduce computational overhead. Although the interference between APs is unavoidable due to the overlapped channel, traditional methods treat APs individually by assuming independence among them. This paper proposes a novel group discriminant (GD)-based AP selection approach for improving location fingerprinting, in which the dependence between APs is considered. The proposed GD approach focuses on measuring the positioning capabilities of each group of APs instead of ranking APs based on their individual importance. It utilizes the risk function from support vector machines (SVMs) to estimate the GD value by maximizing the margin between reference locations. Moreover, this paper proposes a faster version, namely, recursive feature elimination (RFE-GD), to find a suboptimal solution of GD efficiently. This paper applies the proposed algorithms to realistic wireless local area networks (WLANs). Experimental results from two different test beds demonstrate that GD and RFE-GD outperform traditional AP selection schemes, reducing the mean localization error by 40.58%–41.13%. The experiments based on different fingerprinting approaches also confirm the advantages of the proposed algorithms. [ABSTRACT FROM PUBLISHER]