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Biometric fingerprint identification hinges on the reliability of its sensors; however, calibrating and standardizing these sensors poses significant challenges, particularly in regards to repeatability and data diversity. To tackle these issues, we propose methodologies for fabricating synthetic 3D fingerprint targets, or phantoms, that closely emulate real human fingerprints. These phantoms enable the precise evaluation and validation of fingerprint sensors under controlled and repeatable conditions. Our research employs laser engraving, 3D printing, and CNC machining techniques, utilizing different materials. We assess the phantoms' fidelity to synthetic fingerprint patterns, intra-class variability, and interoperability across different manufacturing methods. The findings demonstrate that a combination of laser engraving or CNC machining with silicone casting produces finger-like phantoms with high accuracy and consistency for rolled fingerprint recordings. For slap recordings, direct laser engraving of flat silicone targets excels, and in the contactless fingerprint sensor setting, 3D printing and silicone filling provide the most favorable attributes. Our work enables a comprehensive, method-independent comparison of various fabrication methodologies, offering a unique perspective on the strengths and weaknesses of each approach. This facilitates a broader understanding of fingerprint recognition system validation and performance assessment. [ABSTRACT FROM AUTHOR]

In Moroccan higher education, the integrity of examinations is paramount, yet it faces the persistent challenge of identity impersonation. This form of academic dishonesty not only undermines the credibility of educational institutions but also contributes to the graduation of incompetent students. In an era where artificial intelligence is revolutionizing various sectors, its application to upholding academic integrity is both timely and essential. This paper proposes an advanced deep learning-driven fingerprint verification model specifically designed to combat impersonation in university examinations. Unlike traditional methods, our model leverages the power of Siamese neural networks (SNN), renowned for their effectiveness in learning distinct features and similarities. The model, trained, validated, and tested using the SOCOFing dataset, demonstrated high accuracy and effectiveness in fingerprint identification, crucial for verifying identities in educational exam settings. It achieved an accuracy of 99.29%, and an F1 score of 99.27%, surpassing other systems and significantly contributing to examination integrity in Moroccan higher education. [ABSTRACT FROM AUTHOR]

In several scenarios like forensic and civilian applications, biometric has emerged as a powerful technology for person authentication. Information extracted from different biometric traits is combined by the Multimodal Biometric (MB) solutions, hence showing a high resilience against presentation attacks. Additionally, they offer enhanced biometric performance and increased population coverage that is required for executing larger-scale recognition. By employing Brownian Motion enabled K-Means Algorithm (BM-KMA) and Cosine Swish activation-based Radial Basis Function Neural Network (RBFNN) (CS-RBFNN) methodologies, an MB authentication system centered on overlapped Fingerprints (FPs), Palm Prints (PPs), and finger knuckles (FKs) is proposed here. Primarily, from the publically available datasets, the overlapped FP images and hand images are taken. Next, to separate the PPs and FKs, the Region of Interest (ROI) is estimated for the hand image. Then, pre-processing, feature extraction, and feature reduction are carried out. From the overlapped FP, the noises are removed using BF; after that, the FP's contrast is enriched using SMF-CLAHE for improving the clarity of the minutiae structure of the ridges. Following this, normalization is performed using the Min–Max operation. Minute features are extracted by separating the overlapped FP using BM-KMA, which makes the system from avoidance of system complexity by separating the overlapping. From this, interest features are selected using KRC-PCA. Next, feature fusion is conducted. Finally, CS-RBFNN is wielded to categorize genuine biometrics from imposter ones. Via performance metrics, the proposed system is further affirmed. The outcomes exhibited that the proposed technique surpasses the other prevailing methodologies. [ABSTRACT FROM AUTHOR]

Due to their user-friendliness and reliability, biometric systems have taken a central role in everyday digital identity management for all kinds of private, financial and governmental applications with increasing security requirements. A central security aspect of unsupervised biometric authentication systems is the presentation attack detection (PAD) mechanism, which defines the robustness to fake or altered biometric features. Artifacts like photos, artificial fingers, face masks and fake iris contact lenses are a general security threat for all biometric modalities. The Biometric Evaluation Center of the Institute of Safety and Security Research (ISF) at the University of Applied Sciences Bonn-Rhein-Sieg has specialized in the development of a near-infrared (NIR)-based contact-less detection technology that can distinguish between human skin and most artifact materials. This technology is highly adaptable and has already been successfully integrated into fingerprint scanners, face recognition devices and hand vein scanners. In this work, we introduce a cutting-edge, miniaturized near-infrared presentation attack detection (NIR-PAD) device. It includes an innovative signal processing chain and an integrated distance measurement feature to boost both reliability and resilience. We detail the device's modular configuration and conceptual decisions, highlighting its suitability as a versatile platform for sensor fusion and seamless integration into future biometric systems. This paper elucidates the technological foundations and conceptual framework of the NIR-PAD reference platform, alongside an exploration of its potential applications and prospective enhancements. [ABSTRACT FROM AUTHOR]

In recent years, the advancement of generative techniques, particularly generative adversarial networks (GANs), has opened new possibilities for generating synthetic biometric data from different modalities, including—among others—images of irises, fingerprints, or faces in different representations. This study presents the process of generating synthetic images of human irises, using the recent StyleGAN3 model. The novelty presented in this work consists in producing generated content in both Cartesian and polar coordinate representations, typically used in iris recognition pipelines, such as the foundational work proposed by John Daugman, but hitherto not used in generative AI experiments. The main objective of this study was to conduct a qualitative analysis of the synthetic samples and evaluate the iris texture density and suitability for meaningful feature extraction. During this study, a total of 1327 unique irises were generated, and experimental results carried out using the well-known OSIRIS open-source iris recognition software and the equivalent software, wordlcoin-openiris, newly published at the end of 2023 to prove that (1) no "identity leak" from the training set was observed, and (2) the generated irises had enough unique textural information to be successfully differentiated between both themselves and between them and real, authentic iris samples. The results of our research demonstrate the promising potential of synthetic iris data generation as a valuable tool for augmenting training datasets and improving the overall performance of iris recognition systems. By exploring the synthetic data in both Cartesian and polar representations, we aim to understand the benefits and limitations of each approach and their implications for biometric applications. The findings suggest that synthetic iris data can significantly contribute to the advancement of iris recognition technology, enhancing its accuracy and robustness in real-world scenarios by greatly augmenting the possibilities to gather large and diversified training datasets. [ABSTRACT FROM AUTHOR]

The biometric authentication in today's digital era has become a vital issue in security and privacy. Biometrics is currently becoming more significant specifically for higher security modules. Here, a deep feed-forward neural network-based biometric authentication system (DFFNN_biometric authentication system) is presented for biometric authentication utilizing a biometric fingerprint image. In blockchain network, biometric data are considered and the fingerprint images are fed as input. As an input fingerprint image, minutiae extraction is accomplished. Thereafter, a deep key is generated employing a deep residual network (DRN) for template protection. Thereafter, privacy protection template generation is conducted and the templates thus obtained are stored on a database (DB). On the other hand, a query fingerprint image is given as input in the authentication stage. The processes such as minutiae extraction, deep key generation using DRN and privacy protection template authentication are carried out as like it performed in the blockchain network. Additionally, the matching process is conducted between the output acquired in the authentication phase and image stored in DB. The reputation score is generated from authenticated biometric behavior. The DFFNN is utilized to generate a reputation score. Moreover, the DFFNN_biometric authentication system obtained a maximum accuracy of 90.8%, maximum genuine accept rate (GAR) of 0.936, minimum false acceptance rate (FAR) of 0.588, minimum false rejection rate (FRR) of 0.556 and maximum reputation score of 9.346. [ABSTRACT FROM AUTHOR]

Critical applications ranging from sensitive military data to restricted area access demand selective user authentication. The prevalent methods of tokens, passwords, and other commonly used techniques proved deficient as they can be easily stolen, lost, or broken to gain illegitimate access, leading to data spillage. Since data safety against tricksters is a significant issue nowadays, biometrics is one of the unique human characteristic-based techniques that may give better solutions in this regard. The technique entails biometric authentication of users based on an individual's inimitable physiological or behavioral characteristics to provide access to a specific application or data. This paper provides a detailed description of authentication and its approaches, focusing on biometric-based authentication methods, the primary challenges they encounter, and how they have been addressed. The tabular view shows the benefits and downsides of various multimodal biometric systems, and open research challenges. To put it another way, this article lays out a roadmap for the emergence of multimodal biometric-based authentication, covering both the challenges and the solutions that have been proposed. Further, the urge to develop various multi-trait-based methods for secure authentication and data privacy is focused. Lastly, some multimodal biometric systems comprising fingerprint and iris modalities have been compared based on False Accept Rate (FAR), False Reject Rate (FRR), and accuracy to find the best secure model with easy accessibility. [ABSTRACT FROM AUTHOR]

Current fingerprint identification systems face significant challenges in achieving interoperability between contact-based and contactless fingerprint sensors. In contrast to existing literature, we propose a novel approach that can combine pose correction with further enhancement operations. It uses deep learning models to steer the correction of the viewing angle, therefore enhancing the matching features of contactless fingerprints. The proposed approach was tested on real data of 78 participants (37,162 contactless fingerprints) acquired by national police officers using both contact-based and contactless sensors. The study found that the effectiveness of pose correction and unwarping varied significantly based on the individual characteristics of each fingerprint. However, when the various extension methods were combined on a finger-wise basis, an average decrease of 36.9% in equal error rates (EERs) was observed. Additionally, the combined impact of pose correction and bidirectional unwarping led to an average increase of 3.72% in NFIQ 2 scores across all fingers, coupled with a 6.4% decrease in EERs relative to the baseline. The addition of deep learning techniques presents a promising approach for achieving high-quality fingerprint acquisition using contactless sensors, enhancing recognition accuracy in various domains. [ABSTRACT FROM AUTHOR]

This research work focuses on a Near-Infra-Red (NIR) finger-images-based multimodal biometric system based on Finger Texture and Finger Vein biometrics. The individual results of the biometric characteristics are fused using a fuzzy system, and the final identification result is achieved. Experiments are performed for three different databases, i.e., the Near-Infra-Red Hand Images (NIRHI), Hong Kong Polytechnic University (HKPU) and University of Twente Finger Vein Pattern (UTFVP) databases. First, the Finger Texture biometric employs an efficient texture feature extracting algorithm, i.e., Linear Binary Pattern. Then, the classification is performed using Support Vector Machine, a proven machine learning classification algorithm. Second, the transfer learning of pre-trained convolutional neural networks (CNNs) is performed for the Finger Vein biometric, employing two approaches. The three selected CNNs are AlexNet, VGG16 and VGG19. In Approach 1, before feeding the images for the training of the CNN, the necessary preprocessing of NIR images is performed. In Approach 2, before the pre-processing step, image intensity optimization is also employed to regularize the image intensity. NIRHI outperforms HKPU and UTFVP for both of the modalities of focus, in a unimodal setup as well as in a multimodal one. The proposed multimodal biometric system demonstrates a better overall identification accuracy of 99.62% in comparison with 99.51% and 99.50% reported in the recent state-of-the-art systems. [ABSTRACT FROM AUTHOR]

The term biometrics derives from Greek (bio=life and metrics=measure) and implies the measurement of biological signs. Biometrics is the science of recognizing people based on their physical, behavioral, and physiological attributes, such as fingerprint, face scan, iris, retina, and voice. The present paper aims to develop a study on biometric identification. The major objective of the study is to conduct a survey among the Romanian population on the importance and knowledge of biometric identification methods. This objective was achieved by assessing the knowledge held by the general population of Romania regarding biometric indicators and the degree of adaptability and openness of citizens related to the widest possible implementation of biometrics. The study was based on conducting a quantitative analysis using a questionnaire. Due to the high degree of accessibility, the online environment was chosen as a method of application, distribution being made through social networks. A biometric template digitizes the human body, it has been argued that the collection of biometric identifiers not only interferes with the privacy and right to protection of a person's data, but also with the integrity of an individual's body. In conclusion, the creation and storage of a unique biometric template must be seen in relation to the purpose of the operation. The protection of citizens from criminal activities is a primary obligation of the state. However, it must be exercised with due respect for a number of fundamental ethical values and in the light of modern human rights law. [ABSTRACT FROM AUTHOR]

The deployment of biometrics spans over almost every application that incorporates user authentication. Fingerprint biometrics in particular is by far the most popular choice in biometric authentication systems. However, vulnerabilities such as database compromise, fingerprint reconstruction, and identity fraud have made it crucial to develop highly robust and privacy-preserving authentication systems. Although there have been considerable efforts in the area, security gaps such as dependency on singular point, scale variability and low user recognition performance limit the real-time application of these techniques. In this paper, we address these performance issues and put forth a novel technique for fingerprint template security. The secure templates generated by the proposed technique are computed using many-to-one mapped fully quantized features. A set of rotation and translation independent features are derived using minutiae triplets which makes the proposed secure fingerprint template an alignment-free user template. Each feature type is rotation and translation invariant making the templates free for scale anomalies. The technique essentially hides the true feature information of a fingerprint with the help of a non-invertible transformation function. This enables user identity verification in the transformed domain itself. An extensive result analysis using five fingerprint databases obtained from FVC2002 and FVC2004 has been carried out. The proposed technique delivers a superior performance compared to other noteworthy contributions and therefore proves to be fit for use in various applications involving fingerprint-based identity verification mechanisms. [ABSTRACT FROM AUTHOR]

A fingerprint‐based authentication system provides security to the applications of numerous fields and usually stores minutiae information in the database as a template. It has been observed from the literature that the reconstruction of an original fingerprint is possible from minutiae points information; hence the security of the stored template becomes extremely crucial. Cancellable biometric techniques based on non‐invertible transformation protect the stored template. These techniques prevent the reconstruction of original fingerprint data from the compromised template and avoid unauthorized access to the system. In this paper, a technique based on the non‐invertible transformation to protect a fingerprint template is proposed. In the technique, minutiae locations in a fingerprint are transformed by using the minutiae's original locations and orientation information, and a user keyset. A principal component analysis based approach to align the probe and gallery templates of fingerprint images while matching is also proposed. The evaluation of the proposed technique is carried out on seven different fingerprint databases taken from FVC2000, FVC2002, and FVC2004 databases, and its performance is compared with other existing state‐of‐the‐art techniques in the literature. The comparative performance shows that the proposed technique is highly robust and performs exceptionally well compared to other existing techniques. [ABSTRACT FROM AUTHOR]

Advancements in multimodal biometrics, which integrate multiple biometric traits, promise to enhance the accuracy and robustness of identification systems. This study focuses on improving multimodal biometric identification by using fingerprint and finger vein images as the primary traits. We utilized the "NUPT-FPV" dataset, which contains a substantial number of finger vein and fingerprint images, which significantly aided our research. Convolutional neural networks (CNNs), renowned for their efficacy in computer vision tasks, are used in our model to extract distinct discriminative features. Specifically, we incorporate three popular CNN architectures: ResNet, VGGNet, and DenseNet. We explore three fusion strategies used in security applications: early fusion, late fusion, and score-level fusion. Early fusion integrates raw images at the input layer of a single CNN, combining information at the initial stages. Late fusion, in contrast, merges features after individual learning from each CNN model. Score-level fusion employs weighted aggregation to combine scores from each modality, leveraging the complementary information they provide. We also use contrast limited adaptive histogram equalization (CLAHE) to enhance fingerprint contrast and vein pattern features, improving feature visibility and extraction. Our evaluation metrics include accuracy, equal error rate (EER), and ROC curves. The fusion of CNN architectures and enhancement methods shows promising performance in identifying multimodal biometrics, aiming to increase identification accuracy. The proposed model offers a reliable authentication system using multiple biometrics to verify identity. [ABSTRACT FROM AUTHOR]

Fingerprint recognition technology is widely employed for identity verification and access control across diverse domains in which the quality of fingerprint images is critical for accurate biometric identification. However, fingerprint images can be damaged or incomplete due to various factors such as sensor limitations, environmental conditions, or physical injuries. To address this, several approaches including attention mechanism based autoencoders, multi-kernel sequential and multi-kernel stacked autoencoders, and multi-kernel ensemble autoencoders are proposed to perform reconstruction of incomplete and damaged fingerprints. Attention mechanisms play a crucial role by selectively emphasizing important regions and details, further enhancing information capture. By utilizing different autoencoder models with varying kernel sizes, local details and global context can be effectively captured, resulting in more precise restoration. The adoption of multi-kernel sequential and multi-kernel stacked autoencoders enables the extraction of increasingly abstract features, enhancing the model's ability to capture complex fingerprint characteristics. Utilizing autoencoder-based methods for the reconstruction of damaged or incomplete fingerprint images can enhance the accuracy and reliability of fingerprint identification systems leading to improved security and efficiency in real-world applications. Evaluation is conducted using image quality assessment metrics and feature-based matching is utilized to compute fingerprint matching accuracy. The multi-kernel ensemble autoencoder model produces the best reconstruction output with an average fingerprint matching accuracy of 93.81 %. The findings highlight the effectiveness of the proposed work in achieving high-quality reconstruction output and accurate fingerprint matching. The proposed work can be applied in various domains that include law enforcement, security, forensic analysis, and biometric authentication. [ABSTRACT FROM AUTHOR]

The process of identifying a person using their facial traits is referred to as face recognition, and it is a form of biometric identification. The use of facial recognition might range from that of an entertainment tool to one of a security tool. Even while other forms of biometric identification, such as fingerprints and iris scans, are reliable, they require the active participation of an individual. As a result, criminals cannot rely on them as the most reliable means of verification. When a criminal database, which stores the individual details of a criminal, and facial recognition technology are brought together, it can identify a criminal who is depicted in an image or seen in a video feed. Not only does a criminal recognition system needs to have a high level of accuracy, but it also needs to be able to adapt to significant changes in lighting, occlusion, aging, expressions, and other factors. In this study, they were analyzed and compared with the many methods of face detection and face recognition, such as HAAR cascades, local binary patterns histogram, support vector machines, convolutional neural networks, and ResNet-34. These methods include a variety of different approaches to recognizing faces. An analysis of these strategies is also conducted and then put into practice to those that seem to be the most effective for the designed criminal recognition system. In addition to that, a variety of uses of this criminal recognition in the real world are also discussed. [ABSTRACT FROM AUTHOR]

Fingerprint recognition has emerged as one of the most reliable biometric authentication methods, owing to its uniqueness and permanence. However, the security and confidentiality of the user's data are key considerations in modern biometric systems. In this study, we describe an intelligent computational technique for automatically validating fingerprints for identification and verification purposes. The feature vector is created by fusing Gabor filtering features with deep learning techniques like the faster region-based convolutional neural network (Faster R-CNN). This study uses linear and decision-based median filtering (DBMF) techniques to minimize visual impulse noise. Faster-R-CNN with DBMF was applied to the feature vectors to reduce overfitting problems while improving classification precision and reliability. For fingerprint matching, the Euclidean distance between the associated Harris-SURF feature vectors of two feature points is used to measure feature-matching similarity between two fingerprint images. Furthermore, for fine-tuned matching an iterative technique known as RANSAC (Random Sample Consensus) is used. The experimental results collected from the public-domain fingerprint databases FVC-2002 DB1 and FVC-2000 DB1 show that the proposed design is viable and performs well with an accuracy of 99.43%, MSE value of 43.321%, and an execution time of 3.102 ms which was more exact than existing models. • Reduces image impulse noise using decision-based median and linear filtering. • Feature vectors were processed with Faster-R-CNN and DBMF to alleviate overfitting. • Optimizes the matching procedure and automatically eliminates incorrect matches. • Experimentation with publicly available datasets of FVC2002 DB1 and FVC2000 DB1. [ABSTRACT FROM AUTHOR]

This study presents a novel automatic class attendance system that uses a biometric facial recognition technique based on Raspberry Pi and a camera module. Furthermore, the operation of the detection algorithm is accomplished by calculating the distance between the eyes; distance from the forehead to the chin; distance between the nose and mouth; depth of the eye sockets; shape of the cheekbones; and contour of the lips, ears, and chin. The developed face recognition system works with four major steps: capturing and scanning, extracting, comparing, and matching the captured image. The developed system recognizes students' faces in real time to handle the daily task of tracking students' attendance instead of using a manual method. The Raspberry Pi 3 model B+ microprocessor combined with the open-source libraries of Python, such as OpenCV, and a camera module are implemented in the facial biometric identification and recognition algorithm for recording attendance. The performance of the developed recognition system exhibits a high accuracy of 100% with a fast detection time of 1 s. In addition, the developed system has the ability to capture a student's face at a distance of 1.25 cm from the camera and alongside student's face orientations of 0° and 45°. The developed system performs fast and authentically, and thus, it is worthy of integration into any application. [ABSTRACT FROM AUTHOR]

Biometric authentication systems have recently been gaining increased attention as an integral part of modern civic life. Security surveillance systems encompass broad categories ranging from unlocking mobile phones to personal identification authentication. Most state-of-the-art biometric systems employ fingerprint, face recognition, iris scanner systems, etc. These biometric systems are popular because of their easy-to-use and highly accurate in-person authentication. However, they do not guarantee liveness detection and can be easily deceived. Electroencephalography (EEG)-based biometric systems have dynamic and nonstationary characteristics during liveness, which offer unique, universal, and robust approaches against fraud attacks and thus present a high potential for secure biometric authentication systems. This study aimed to investigate the performance of a new framework for an FSST-ICA-based EEG-biometric authentication approach over motor imagery (MI) signals using an ensemble of LSTM deep models. The Fourier synchrosqueezed transform (FSST) was performed to implement feature extraction by analyzing the time–frequency (TF) matrix properties of the EEG signals. Synchrosqueezing transform was adopted as a feasible way to provide compact component localization capabilities for dynamic and nonstationary EEG signals with detailed spectral properties in the TF domain. Independent component analysis (ICA) was also carried out to decompose EEG multichannel sources in order to improve the "true acceptance rate" (TAR) and "false acceptance rate" (FAR) performance, as well as the correct recognition rate. The biometric authentication outcomes indicated that a high average accuracy (99.54%), sensitivity (99.81%), and specificity (99.41%) had been obtained regarding the "one-versus-others" discrimination among seven individuals via MI-EEG raw and subband (< 30 Hz) signals. Furthermore, high average TAR (97.8%) and low FAR (0%) values demonstrated robustness against multiple trials. To the best of our knowledge, an FSST-ICA framework for the EEG-based biometric approach using an ensemble of LSTM deep models has not been explored to date in the current literature. The study presents a highly secure and low-cost biometric system having broad fields of application. The effectiveness of the proposed framework over the spatiotemporal dynamics of the MI-EEGs was also evaluated by examining the broad statistical methods. This appears to be the first attempt to validate the discrimination of individuals in both raw and time–frequency features using extensive statistical analysis. [ABSTRACT FROM AUTHOR]

In recent times, there has been a noticeable increase in the application of human biometrics for user authentication in various domains, such as online banking. However, the use of biometric systems poses security risks and the potential for misuse, primarily due to the storage of original templates in databases. To tackle this issue, the concept of cancelable biometrics has emerged as a reliable method utilizing one-way encryption. Several algorithms have been developed to implement cancelable biometrics, incorporating visual representations of single or multiple biometrics. This research proposes a cancelable biometric system that utilizes deep learning techniques to generate two encrypted modalities, namely text and image, using facial and fingerprint biometrics acquired from a smartphone. The system consists of two main stages: a visual encoder and a text encoder. The visual encoder converts the fingerprint style into a facial representation, creating a cancelable template to ensure the potential for cancelation. The resulting visual template is then processed by the text encoder, which employs hashing techniques to generate a corresponding text template. User authentication is automatically verified by utilizing the generated templates through Siamese networks. [ABSTRACT FROM AUTHOR]

Crime rates in our contemporary technological age have been rising at an alarming pace. Newer methods are needed to determine who the perpetrator and victim are in these situations. By comparing a person's recorded information with their own unique characteristics, automated biometric systems may determine who they are. Biometric identifiers like fingerprint and iris scanning are among the most reliable ways. Due to the vulnerability of soft tissues, these procedures cannot be depended on in the event of widespread calamities such as fires or gas leaks. Therefore, improved individual identification necessitates a biometric system based on the hard tissue. Thus, ameloglyphics was created to serve as a key tool in forensic odontology, particularly in the case of huge fatalities caused by natural catastrophes. [ABSTRACT FROM AUTHOR]

Identifying an individual from accelerometry data collected during walking without reliance on step-cycle detection has not been achieved with high accuracy. We propose an open-source reproducible method to: (1) create a unique, person-specific "walking fingerprint" from a sample of un-landmarked high-resolution data collected by a wrist-worn accelerometer; and (2) predict who an individual is from their walking fingerprint. Accelerometry data were collected during walking from 32 individuals (23–52 y.o., 19 females) for at least 380 s each. For this study's purpose, data are not landmarked, nor synchronized. Individual walking fingerprints were created by: (1) partitioning the accelerometer time series in adjacent, non-overlapping one-second intervals; (2) transforming all one-second interval data for a given individual into a three-dimensional (3D) image obtained by plotting each one-second interval time series by the lagged time series for a series of lags; (3) partitioning these resulting participant-specific 3D images into a grid of cells; and (4) identifying the combinations of cells (areas in the 3D image) that best predict the individual. For every participant, the first 200 s of data were used as training and the last 180 s as testing. This approach does not use segmentation methods for individual strides, which reduces dependence on complementary algorithms and increases its generalizability. The method correctly identified 100 % of the participants in the test data and highlighted unique features of walking that characterize the individuals. Predicting the identity of an individual from their walking pattern has immediate implications that can complement or replace those of actual fingerprinting, voice, and image recognition. Furthermore, as walking may change with age or disease burden, individual walking fingerprints may be used as biomarkers of change in health status with potential clinical and epidemiologic implications. • No methods exist to identify individuals from high-resolution, un-landmarked, accelerometry-derived data from walking. • Time-series accelerometry data from walking can be transformed into a 3D image. • Predictors can be derived from these 3D images and used in classification algorithms. • Multivariate logistic regression using these predictors correctly identifies 100 % of subjects in a study of 32 individuals. [ABSTRACT FROM AUTHOR]

In recent years, biometric authentication systems have remained a hot research topic, as they can recognize or authenticate a person by comparing their data to other biometric data stored in a database. Fingerprints, palm prints, hand vein, finger vein, palm vein, and other anatomic or behavioral features have all been used to develop a variety of biometric approaches. Finger vein recognition (FVR) is a common method of examining the patterns of the finger veins for proper authentication among the various biometrics. Finger vein acquisition, preprocessing, feature extraction, and authentication are all part of the proposed intelligent deep learning-based FVR (IDL-FVR) model. Infrared imaging devices have primarily captured the use of finger veins. Furthermore, a region of interest extraction process is carried out in order to save the finger part. The shark smell optimization algorithm is used to tune the hyperparameters of the bidirectional long–short-term memory model properly. Finally, an authentication process based on Euclidean distance is performed, which compares the features of the current finger vein image to those in the database. The IDL-FVR model surpassed the earlier methods by accomplishing a maximum accuracy of 99.93%. Authentication is successful when the Euclidean distance is small and vice versa. [ABSTRACT FROM AUTHOR]

The study introduces a new threshold method based on a neutrosophic set. The proposal applies the neutrosophic overset and underset concepts for thresholding the image. The global threshold method and the adaptive threshold method were used as the two types of thresholding methods in this article. Images could be symmetrical or asymmetrical in professional disciplines; the government maintains facial image databases as symmetrical. General-purpose images do not need to be symmetrical. Therefore, it is essential to know how thresholding functions in both scenarios. Since the article focuses on biometric image data, face and fingerprint data were considered for the analysis. The proposal provides six techniques for the global threshold method based on neutrosophic membership, indicating neutrosophic T F overset ( NO T F ), neutrosophic T I overset ( NO T I ), neutrosophic T I F overset ( NO T I F ), neutrosophic T F underset ( NU T F ), neutrosophic T I underset ( NU T I ), neutrosophic T I F underset ( NU T I F ); similarly, in this study, the researchers generated six novel approaches for the adaptive method. These techniques involved an investigation using biometric data, such as fingerprints and facial images. The achievement was 98 % accurate for facial image data and 100 % accurate for fingerprint data. [ABSTRACT FROM AUTHOR]

The method of identifying a person using behavioral attributes, such as fingerprints, eye structures, or facial characteristics is known as biometric person identification. The procedure utilizes complex algorithms to analyze and compare biometric data against recorded templates. The purpose of this research is to develop a novel multimodal image fusion model for feature extraction based on biometric person identification. To build an intelligent biometric person identification framework, we provide a new Versatile Convolutional Neural Network (VCNN) technique for obtaining vital characteristics using multimodal data. The features gathered from modalities are combined and provided into the (Multi-Kernel Support Vector Machine) MKSVM classifier for classification by combining ensemble learning for classification with deep knowledge for extracting characteristics; this method offers precise biometric person identification. Initially, we gathered a Dataset that contains two biometrics, such as the Faces94 database for face images and fingerprints gathered fromFVC2002, to train our suggested model. We employed histogram equalization to pre-process the gathered raw data, it improves the quality of the obtained data. The finding assessment phase evaluates the suggested model performance using various metrics. We compared the recommended technique to various existing methods to assess its effectiveness. The experimental findings demonstrate that the recommended model outperforms conventional approaches. [ABSTRACT FROM AUTHOR]

Biometric security methods deploy any type of biometric recognition for safety purposes. Different from unimodal biometric system that uses single trait for security, the multimodal biometrical authentication (MBA) system deploys two or additional diverse biometric traits for authentication. In addition, MBA systems incurs high cost and have high level of security, as 2 biometric traits are complex for intruders to counterfeit more than a single trait. A new Ensemble Recognition Model with Optimal Training for Multimodal Biometric Authentication (ERMOTMBA) model is proposed in this work using Self Upgraded NBO (SU-NBO) algorithm. At first, median filtering is deployed to pre-process the palm print image, finger vein image, finger knuckle image, iris image and finger print image. The features extracted for palm print, finger vein, finger knuckle, iris and finger print are as follows: Palm print – extracting statistical features and line features, Finger vein – extracting bifurcation point and improved LGBP, Finger knuckle- Global feature and local feature, Iris- extracting 2D Gabor kernel and polynomial filtering, Finger print- Improved Minutiae feature (Binarization and thinning). After the extraction of feature set, enhanced feature level fusion (FLF) takes place under correlation basis. Finally, EC (with the combination of DBN, CNN and Bi-LSTM) is exploited, whose outputs are averaged to finalize the recognition outcome. Especially, proposing a training model for tuning the EC weights, and that is termed as SU-NBO algorithm. [ABSTRACT FROM AUTHOR]

Handwritten signatures in biometric authentication leverage unique individual characteristics for identification, offering high specificity through dynamic and static properties. However, this modality faces significant challenges from sophisticated forgery attempts, underscoring the need for enhanced security measures in common applications. To address forgery in signature-based biometric systems, integrating a forgery-resistant modality, namely, noninvasive electroencephalography (EEG), which captures unique brain activity patterns, can significantly enhance system robustness by leveraging multimodality's strengths. By combining EEG, a physiological modality, with handwritten signatures, a behavioral modality, our approach capitalizes on the strengths of both, significantly fortifying the robustness of biometric systems through this multimodal integration. In addition, EEG's resistance to replication offers a high-security level, making it a robust addition to user identification and verification. This study presents a new multimodal SignEEG v1.0 dataset based on EEG and hand-drawn signatures from 70 subjects. EEG signals and hand-drawn signatures have been collected with Emotiv Insight and Wacom One sensors, respectively. The multimodal data consists of three paradigms based on mental, & motor imagery, and physical execution: i) thinking of the signature's image, (ii) drawing the signature mentally, and (iii) drawing a signature physically. Extensive experiments have been conducted to establish a baseline with machine learning classifiers. The results demonstrate that multimodality in biometric systems significantly enhances robustness, achieving high reliability even with limited sample sizes. We release the raw, pre-processed data and easy-to-follow implementation details. [ABSTRACT FROM AUTHOR]

This research study suggests a sophisticated electronic protection solution that uses biometric authentication to reduce the possibility of exam paper leaks. Academic integrity is facing serious issues due to the growing vulnerability of traditional ways of securing examination papers to breaches, which are caused by the expansion of digital technology. Using biometric authentication provides a reliable way to verify the identity of authorized workers and keep private exam materials out of the hands of unauthorized individuals. To provide a multi-layered security strategy, the system incorporates multiple biometric modalities, including fingerprint, iris, and facial recognition. Furthermore, sophisticated encryption techniques are used to protect data storage and transfer. In addition to strengthening exam paper security, the suggested approach expedites administrative procedures, increasing productivity and lowering the possibility of human error. The design, implementation, and evaluation of the suggested system are covered in this work, with an emphasis on how well it detects and stops exam paper leaks while preserving user comfort and system scalability. A buzzer alarm and a message to the university are sent in the event that an unauthorized attempt to access the documents is discovered, alerting the appropriate authorities. [ABSTRACT FROM AUTHOR]

This study focuses on enhancing office security through a smart door system, designed to protect sensitive documents and critical data. Emphasizing exclusive access for authorized personnel, the system integrates advanced biometric authentication, predominantly facial recognition. The project's aim is to optimize face recognition using convolutional neural network (CNN) techniques, identifying the best preprocessing methods and hyperparameter settings. A significant aspect of the research involves developing a smart door system with remote authentication and control capabilities via internet connectivity. Employing transfer learning with MobileNet V2, the study presents a compact model tailored for the Raspberry Pi platform. The model utilizes a dataset with five facial recognition classes and an additional class for unknown faces, ensuring a diverse representation. The trained model achieved a high accuracy (0.9729) and low loss (0.09). System evaluation revealed an overall accuracy of 0.96, perfect recall (1.00), and a precision of 0.897. These results demonstrate the system's efficacy in secure access control, making it a viable solution for contemporary office environments. [ABSTRACT FROM AUTHOR]

Single Image Super Resolution (SISR) aims to recover high-frequency details of an image from its low-resolution form. It is a highly challenging problem since low-resolution image is blur and contains noise. The application of SISR can be found in various fields, such as microscopic image analysis, medical imaging, security and surveillance imaging, biometric image identification, hyper spectral imaging and text image super-resolution. Convolutional Neural Networks (CNNs) are the most widely used technique for SISR. Most of the previous CNN-based SISR methods blindly increase the network depth to achieve good performance, leading to increased computational cost. They train feed-forward convolutional neural networks using a per-pixel loss between the output and ground-truth images. U-SRN model is proposed for single-image super-resolution, consisting of a contracting path to capture context and a symmetric expanding path that enables precise localization. Per-pixel loss is replaced with perceptual loss, which gives visually pleasing results. Extensive experiments show that the U-SRN model performs excellently and can perform multi-scale tasks. The model is trained on the DIV2K dataset. It is a benchmark dataset in the field of super-resolution, which consists of 1,000 high-quality images. Set 5, Set 14, Urban 100, and BSD 100 are the benchmark super-resolution datasets used for testing. For the upscaling factor of 4, the average gains on PSNR, SSIM and IFC achieved by U-SRN using Urban 100 dataset are 0.24 dB, 0.0037 and 0.025 higher than the next best approach. The results indicate that U-SRN performs better as compared to other state-of-art methods in terms of PSNR, SSIM and IFC on all datasets. The other performance analysis criterion, such as PI (4.12), FSIM (0.971), and SRQC (0.954), indicate the superiority of the model. The network has limited number of parameters and performs fast execution. [ABSTRACT FROM AUTHOR]

Multi-modal ocular biometrics has recently garnered significant attention due to its potential in enhancing the security and reliability of biometric identification systems in non-constrained scenarios. However, accurately and efficiently segmenting multi-modal ocular traits (periocular, sclera, iris, and pupil) remains challenging due to noise interference or environmental changes, such as specular reflection, gaze deviation, blur, occlusions from eyelid/eyelash/glasses, and illumination/spectrum/sensor variations. To address these challenges, we propose OcularSeg, a densely connected encoder–decoder model incorporating eye shape prior. The model utilizes Efficientnetv2 as a lightweight backbone in the encoder for extracting multi-level visual features while minimizing network parameters. Moreover, we introduce the Expectation–Maximization attention (EMA) unit to progressively refine the model's attention and roughly aggregate features from each ocular modality. In the decoder, we design a bottom-up dense subtraction module (DSM) to amplify information disparity between encoder layers, facilitating the acquisition of high-level semantic detailed features at varying scales, thereby enhancing the precision of detailed ocular region prediction. Additionally, boundary- and semantic-guided eye shape priors are integrated as auxiliary supervision during training to optimize the position, shape, and internal topological structure of segmentation results. Due to the scarcity of datasets with multi-modal ocular segmentation annotations, we manually annotated three challenging eye datasets captured in near-infrared and visible light scenarios. Experimental results on newly annotated and existing datasets demonstrate that our model achieves state-of-the-art performance in intra- and cross-dataset scenarios while maintaining efficient execution. [ABSTRACT FROM AUTHOR]

With the development of biometric identification technology, finger vein identification has received more and more widespread attention for its security, efficiency, and stability. However, because of the performance of the current standard finger vein image acquisition device and the complex internal organization of the finger, the acquired images are often heavily degraded and have lost their texture characteristics. This makes the topology of the finger veins inconspicuous or even difficult to distinguish, greatly affecting the identification accuracy. Therefore, this paper proposes a finger vein image recovery and enhancement algorithm using atmospheric scattering theory. Firstly, to normalize the local over-bright and over-dark regions of finger vein images within a certain threshold, the Gamma transform method is improved in this paper to correct and measure the gray value of a given image. Then, we reconstruct the image based on atmospheric scattering theory and design a pixel mutation filter to segment the venous and non-venous contact zones. Finally, the degraded finger vein images are recovered and enhanced by global image gray value normalization. Experiments on SDUMLA-HMT and ZJ-UVM datasets show that our proposed method effectively achieves the recovery and enhancement of degraded finger vein images. The image restoration and enhancement algorithm proposed in this paper performs well in finger vein recognition using traditional methods, machine learning, and deep learning. The recognition accuracy of the processed image is improved by more than 10% compared to the original image. [ABSTRACT FROM AUTHOR]

Objectives: To assess the accuracy, repeatability, and reproducibility of T1 and T2 relaxation time measurements by three-dimensional magnetic resonance fingerprinting (3D MRF) using various dictionary resolutions. Methods: The ISMRM/NIST phantom was scanned daily for 10 days in two 3 T MR scanners using a 3D MRF sequence reconstructed using four dictionaries with varying step sizes and one dictionary with wider ranges. Thirty-nine healthy volunteers were enrolled: 20 subjects underwent whole-brain MRF scans in both scanners and the rest in one scanner. ROI/VOI analyses were performed on phantom and brain MRF maps. Accuracy, repeatability, and reproducibility metrics were calculated. Results: In the phantom study, all dictionaries showed high T1 linearity to the reference values (R2 > 0.99), repeatability (CV < 3%), and reproducibility (CV < 3%) with lower linearity (R2 > 0.98), repeatability (CV < 6%), and reproducibility (CV ≤ 4%) for T2 measurement. The volunteer study demonstrated high T1 reproducibility of within-subject CV (wCV) < 4% by all dictionaries with the same ranges, both in the brain parenchyma and CSF. Yet, reproducibility was moderate for T2 measurement (wCV < 8%). In CSF measurement, dictionaries with a smaller range showed a seemingly better reproducibility (T1, wCV 3%; T2, wCV 8%) than the much wider range dictionary (T1, wCV 5%; T2, wCV 13%). Truncated CSF relaxometry values were evident in smaller range dictionaries. Conclusions: The accuracy, repeatability, and reproducibility of 3D MRF across various dictionary resolutions were high for T1 and moderate for T2 measurements. A lower-resolution dictionary with a well-defined range may be adequate, thus significantly reducing the computational load. Key Points: • A lower-resolution dictionary with a well-defined range may be sufficient for 3D MRF reconstruction. • CSF relaxation times might be underestimated due to truncation by the upper dictionary range. • Dictionary with a higher upper range might be advisable, especially for CSF evaluation and elderly subjects whose perivascular spaces are more prominent. [ABSTRACT FROM AUTHOR]

A number of studies have used the measurement of density of epidermal ridges on human fingerprints (or average epidermal ridge breadth if the value is expressed in reverse) as a metric to estimate the age of the originator of the imprint at the time of growth and sex at maturity. A methodologically unsolved question is how the number of ridges measured together within one segment (or the length of the line segment across which the ridges are counted) affects the results. In this study, we therefore investigated how the count of ridges measured together within one segment, as well as the count of averaged segments per subject, when averaged, affect the resulting values of mean epidermal ridge breadth. Moreover, we investigated how different regions on the human fingers and palms differ in this respect. Using a cross-sectional sample of 90 school children (45 girls and 45 boys, age range from 6 to 16 years) from South Moravia, we compared the differences in epidermal ridge breadth in 29 different hand regions, particularly in terms of the degree of age differences. The results show that different regions on the hand vary significantly in the effect of age which might have consequences for estimating age and sex based on these epidermal ridge breadth measurements. However, the ability to statistically distinguish age or sex groups is affected by the number of measurement units (ridges, fingerprints) used to calculate mean epidermal ridge breadth (MRB). Therefore, in future research, it would be advisable to introduce computation with interval estimates of MRB or a hierarchical approach directly accounting for individual epidermal ridges. [ABSTRACT FROM AUTHOR]

This paper problematises the 2007 amendments to Article 5 of the Police Powers and Duties Law (PPDL) in Turkey that categorises all citizens as 'potential suspects' through fingerprinting technology. The amended article requires everyone to submit fingerprint samples when applying for certain official documents such as driver's licences, passports, and ID cards. Consequently, the result has been dramatic: the police have so far proactively recorded more than 60 million people's fingerprints in the process of issuing these documents. Yet, there has been no research into this phenomenon. This paper suggests that this sort of biometric police surveillance is not a recent development, rather part of a long tradition within policing 'Turkish' national interests. Following Foucault's genealogical methodology, the paper argues that the governability of a large heterogeneous population across a vast territory has always demanded biometric policing technologies, addressing biopolitical proximity between the capacity building of modern security apparatus and identifying the unknown masses. Studying the historical data comparatively reveals that fingerprinting first started with recording exceptional groups such as criminals and convicts in Europe, while from the late Ottoman Empire to modern Turkey, large sections of the population have always persistently been targeted by police regulations. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of poor biometric recognition performance that is caused by greatly increased variations among the hand vein patterns of the same individuals as a result of using different imaging devices under different acquisition conditions and presents a novel solution based on two-stage coarse-to-fine matching. In particular, a global pattern descriptor is proposed as a geometrical reference for optimum image segmentation of vein patterns without significant over-segmentation and under-segmentation. In order to accommodate large cross-device variations, a control parameter is introduced to allow adjustment of segmented vein patterns, thereby enabling not only intra-class pattern similarities but also inter-class pattern dissimilarities to be increased. Furthermore, overlapping of principal vein patterns is proposed as a criterion for global coarse matching to reduce the number of candidates for identification, and distinctive efficient robust features are employed to provide a biological vision-based descriptor of salient local pattern characteristics for fine matching. Using a large dataset of 2000 cross-device hand vein images captured from two different near-infrared imaging devices and 100 hands, the efficacy of the proposed approach for a cross-device biometric system is demonstrated, with a recognition performance shown to be compatible to that of a single-device biometric system. [ABSTRACT FROM AUTHOR]

Automatic Teller Machine (ATM) offers rapid and user-friendly avenues to reach their bank accounts and engage in financial operations. A crucial component of ATM security is the "Personal Identification Number (PIN) or password". This PIN or password serves as a fundamental element in safeguarding and preserving customers' financial data from unauthorized entry. Within the financial realm, an ongoing necessity exists to enhance security measures. In the realm of identity verification, modern ATM systems traditionally require the combination of an access card and the input of a PIN. However, the landscape has evolved with the emergence of cutting-edge biometric authentication methods like fingerprint scanning, retina recognition, and facial identification. These innovations have significantly mitigated the security vulnerabilities previously associated with ATMs. To surmount such challenging factors, a novel multimodal biometric-based authentication is introduced for ATM transactions. Traditionally, the MultiBank Provider (Pvt Company) provides an ATM card with all bank access for an individual. With the help of ATM machines, multimodal authentication is accomplished by using the Multichannel EfficientNet B7 with Attention Mechanism (MEB7-AM), in which each channel carries information about each image from the Face, Retina, Fingerprint, and spectrogram. Once it is done, a single pin is required to select the bank. Further, from the selected bank with proper credentials, the money is withdrawn from the ATM machine. Lastly, the efficacy of the model is analyzed using various measures and compared among existing methodologies. Therefore, the proposed system provides the precise results of better authentication for ATM machines. [ABSTRACT FROM AUTHOR]

Fingerprint patterns are unique and reliable for identification. This research paper focuses on a comparative analysis to determine the inheritance of fingerprint patterns within Indian families. The sample collection process for this comparative analysis involved working with 10 families. The study aims to gain insights into the hereditary aspects of fingerprint characteristics among the Indian population. The research methodology involves the collection of fingerprints and the analysis of patterns using microscopes, magnifying lenses, and software. The results reveal both class and individual characteristics within fingerprints, contributing to our understanding of dermatoglyphics. The average class characteristics percentage totals around 71.5%, with an average individual characteristic percentage of approximately 13.6%. The research has implications for forensic investigations, genetics, and personal identification systems. Further studies with larger sample sizes and genetic analysis integration are recommended for future research. [ABSTRACT FROM AUTHOR]

Principal Component Analysis (PCA) is the best face recognition method. This research suggests PCA for fingerprint and signature recognition. Simple image processing transforms like DCT, 2D-DCT, DWT, SWT, 2D-SWT, SVD (Singular Vector Decomposition), Entropy, and Rank can be used for feature extraction. These transforms and measures are utilized with PCA as a feature extraction module to construct uni-modal and multimodal biometric systems using face, fingerprint, and signature modalities. Most PCA biometrics systems compare stored template to claimed identification using Euclidean distance. This paper proposes matching modules using similarity and dissimilarity measures viz. Absolute Pearson's Correlation Coefficient (APCC), Absolute Uncentered Pearson's Correlation Coefficient (AUPCC), Bray Curtis Distance (BC), Canberra distance (CB), Chebyshev Distance (CBS), Chessboard Distance (CSB), City block or Manhattan distance (CTB), Cross Correlation (CC), Dot product (DP), Euclidean distance (EUC), Extended Jaccard Distance (EJ), Hamming Distance (HM), Harmonically Summed Euclidean distance (HSEUC), Kendall Correlation Coefficient (KCC), Mahalanobis Distance (MH), Minimum Coordinate Difference (MCD), Minkowiski distance (MNK), Multivariate Kurtosis Coefficient (MVK), Multivariate Skew (MVS), Normalized City Block or Manhattan distance (NCTB), Normalized Cross-correlation (NCC), Normalized Euclidean distance (NEUC), Pearson's Cosine Distance (PCOS), Pearson's Correlation Coefficient (PCC), Pearson's Absolute Value Dissimilarity (PAVD), Pearson's Linear Dissimilarity (PLDISS), Spearman Correlation Coefficient (SCC), Standardized Euclidean Distance (SEUC), Uncentered Pearson's Correlation Coefficient (UPCC), Wave-Hedges Distance (WVH). This study again discusses score level fusion of face, fingerprint, and signature using sum and max rules, z-score normalization, and decision level fusion using AND rule. [ABSTRACT FROM AUTHOR]

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