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Developments in multimedia and mobile communication technologies and in mobilized, personalized information security has benefitted various sectors of society, as traditional identification technologies are often complicated. In response to the sharing economy and the intellectualization of automotive electronics, major automobile companies are using biometric recognition to enhance the safety, uniqueness, and convenience of their vehicles. This study uses a deep learning-based finger-vein identification system for carputer systems. The proposed enhancement edge detection adapts to the detected fingers' rotational and translational movements and to interference from external light and other environmental factors. This study also determines the effect of preprocessing methods on the system's effectiveness. The experimental results demonstrate that the proposed system allows more accurate identification of 99.1% and 98.1% in various environments, using the FV-USM and SDUMLA-HMT public datasets. As results, the contribution of system is high accuracy and stability for more sanitary, contactless applications makes it eminently suited for use during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

With the advancement of multimedia and digital technology, traditional vehicles are gradually replaced by intelligent ones. As people attach increasing importance to convenience and security, traditional keys and password locks are also being replaced. Although radio frequency identification (RFID) is convenient, some researches have pointed out security concerns on its unlocking technology. In view of this, the finger-vein patterns to be used as a keyless vehicle access control system for intelligent vehicles (iVehicles) is presented. Semantic segmentation DeepLabv $3^{+}$ based on deep learning (DL) was integrated to filter out the background noise and enhance processing stability. Also, the enhanced maximum curvature (EMC) method to extract vein features was adopted, and best matching regional scores (SMRS) and support vector machines (SVMs) were utilized for hierarchical feature extraction. Lastly, these methods were actualized on a low-level embedded platform Raspberry Pi, with which cloud computing was used to realize real-time identification. When three images were used for training and three for testing, the results showed that the proposed hierarchical vein verification technique had an equal error rate (EER) of 0.84% and 0.47% in the NIU-MIT and FV-USM datasets, respectively. [ABSTRACT FROM AUTHOR]

Developments in multimedia and mobile communication technologies and in mobilized, personalized information security has benefitted various sectors of society, as traditional identification technologies are often complicated. In response to the sharing economy and the intellectualization of automotive electronics, major automobile companies are using biometric recognition to enhance the safety, uniqueness, and convenience of their vehicles. This study uses a deep learning-based finger-vein identification system for carputer systems. The proposed enhancement edge detection adapts to the detected fingers’ rotational and translational movements and to interference from external light and other environmental factors. This study also determines the effect of preprocessing methods on the system’s effectiveness. The experimental results demonstrate that the proposed system allows more accurate identification of 99.1% and 98.1% in various environments, using the FV-USM and SDUMLA-HMT public datasets. As results, the contribution of system is high accuracy and stability for more sanitary, contactless applications makes it eminently suited for use during the COVID-19 pandemic.

Multimodal biometric-based recognition has been an active topic because of its higher convenience in recent years. Due to high user convenience of finger, finger-based personal identification has been widely used in practice. Hence, taking Finger-Print (FP), Finger-Vein (FV) and Finger-Knuckle-Print (FKP) as the ingredients of characteristic, their feature representation were helpful for improving the universality and reliability in identification. To usefully fuse the multimodal finger-features together, a new robust representation algorithm was proposed based on hierarchical model. Firstly, to obtain more robust features, the feature maps were obtained by Gabor magnitude feature coding and then described by Local Binary Pattern (LBP). Secondly, the LGBP-based feature maps were processed hierarchically in bottom-up mode by variable rectangle and circle granules, respectively. Finally, the intension of each granule was represented by Local-invariant Gray Features (LGFs) and called Hierarchical Local-Gabor-based Gray Invariant Features (HLGGIFs). Experiment results revealed that the proposed algorithm is capable of improving rotation variation of finger-pose, and achieving lower Equal Error Rate (EER) in our homemade database. [ABSTRACT FROM AUTHOR]

Suffering from uneven illumination and variation of finger position, it is still a tough challenge to effectively distinguish the vein networks and nonvenous regions in a finger-vein image. Methods based on active contour have achieved an excellent result in medical image segmentation, despite facing several challenges such as vulnerable to the initial contour and prone to local minimum. In this article, we propose a novel method which is effective for finger-vein image segmentation based on active contour. Since venous and nonvenous areas in captured finger-vein images are hard to distinguish, we design a dehazing algorithm and an edge fitting term to improve the segmentation procedure. Moreover, we employ the kernel fuzzy C-means (KFCM) algorithm to conduct the initialization, which is able to solve the problem that the active contour-based methods are susceptible to initial contours. The experimental results show that compared with latest methods, the proposed method achieves a better performance in segmenting finger-vein images and is able to improve the recognition accuracy of finger-vein identification system. [ABSTRACT FROM AUTHOR]

This paper presents a novel deep learning-based method that integrates a Convolutional Auto-Encoder (CAE) with support vector machine (SVM) for finger-vein verification. The CAE is used to learn the features from finger-vein images, and the SVM is used to classify finger vein from these learned feature codes. The CAE consists of a finger-vein encoder, which extracts high-level feature representation from raw pixels of the images, and a decoder which outputs reconstruct finger-vein images from high-level feature code. As an effective classifier, SVM is introduced in this paper to classify the feature code which is obtained from CAE. Experiments prove that the proposed deep learning-based approach has superior performance in learning features than traditional method without any prior knowledge, presenting a good potential in the verification of finger vein. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The use of human finger-vein traits for the purpose of automatic user recognition has gained a lot of attention in recent years. Current state-of-the-art techniques can provide relatively good performance, yet they are strongly dependent upon the quality of the analyzed finger-vein images. In this paper, we propose a convolutional-neural-network-based finger-vein identification system and investigate the capabilities of the designed network over four publicly available databases. The main purpose of this paper is to propose a deep-learning method for finger-vein identification, which is able to achieve stable and highly accurate performance when dealing with finger-vein images of different quality. The reported extensive set of experiments show that the accuracy achievable with the proposed approach can go beyond 95% correct identification rate for all the four considered publicly available databases. [ABSTRACT FROM AUTHOR]

Region of interest (ROI) localization is one of the key preprocessing technologies for a finger-vein identification system, so an effective ROI definition can improve the matching accuracy. However, due to the impact of uneven illumination, equipment noise, as well as the distortion of finger position, etc., these make accurate ROI localization a very difficult task. To address these issues, in this paper, we propose a robust finger-vein ROI localization method, which is based on the 3 σ criterion dynamic threshold strategy. The proposed method includes three main steps: First, the Kirsch edge detector is introduced to detect the horizontal-like edges in the acquired finger-vein image. Then, the obtained edge gradient image is divided into four parts: upper-left, upper-right, lower-left, and lower-right. For each part of the image, the three-level dynamic threshold, which is based on the 3 σ criterion of the normal distribution, is imposed to obtain more distinct and complete edge information. Finally, through labeling the longest connected component at the same horizontal line, two reliable finger boundaries, which represent the upper and lower boundaries, respectively, are defined, and the ROI is localized in the region between these two boundaries. Extensive experiments are carried out on four different finger-vein image datasets, including three publicly available datasets and one of our newly developed finger-vein datasets with 37,080 finger-vein samples and 1030 individuals. The experimental results indicate that our proposed method has very competitive ROI localization performance, as well as satisfactory matching results on different datasets.

The highly random manner in which veins spread along a finger, their immunity to counterfeiting, active liveness, and user friendliness make finger veins the best choice for a biometric identification system (BIS). In this paper, veins of six fingers of two hands of a person are used to develop a secure, reliable, and robust multimodal BIS (MBIS). The main structure of the proposed MBIS is based on the effective combination of rank- and decision-level fusion. In the training step, the power (weight) of each single modality is estimated by extracting the information that lies in the cumulative match characteristic (CMC) curve. The testing step consists of two main parts. In the first part, the region of the finger vein is extracted by using a simple method, and then the binarized statistical image features (BSIFs) algorithm is used to extract feature vectors. In the second part, final decision for the test input probe is made by generating ' top rank-decision matrix', which fuses the information of each biometric identifier in the hybrid rank-decision level. The obtained results show that proposed method is more reliable and accurate than other fusion techniques at the post-classification fusion level. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Due to the uniqueness of the finger-vein patterns hidden beneath the skin, forgery is very difficult. Providing fast and accurate finger-vein recognition represents the answer to biometric security system as we need more secure and reliable authentication methods. However, the finger-vein based recognition system is limited by the storage space and time complexity, which significantly reduce the accuracy of the identification. In this paper, we present an effective method of matching in a finger-vein recognition system to overcome the disadvantage of requiring significant data storage and heavy CPU computation requirements. Our proposed solution involved considering special points characterizing complex finger-vein information and their connections, thereby retaining only the evidence related to matching to perform subsequent identification. Experimental results show that our method achieves robust matching with an error rate of 0.216 % and confirm that the proposed mechanism can reduce the quantity of data that requires storage and maintain a certain level of authentication accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a real-time image acquisition system with an improved image quality assessment module to acquire high-quality near infrared (NIR) images. Thermal imaging plays a vital role in a wide range of medical and military applications. The demand for high-throughput image acquisition and image processing has continuously increased especially for critical medical and military purposes where executions under real-time constraints are required. This work implements an NIR image quality assessment module, which utilizes improved two-dimensional entropy and mask-based edge detection algorithms. The effectiveness of the proposed image quality assessment algorithms is demonstrated through the implementation of a complete finger-vein biometric system. The proposed model is implemented as an embedded system on a field programmable gate array prototyping platform. By including the image quality assessment module, the proposed system is able to achieve a recognition accuracy of 0.87 % equal error rate, and can handle real-time processing at 15 frames/s (live video rate). This is achieved through hardware acceleration of the proposed image quality assessment algorithms via a novel streaming architecture. [ABSTRACT FROM AUTHOR]

This paper presents a new scheme to improve the performance of finger-vein identification systems. Firstly, a vein pattern extraction method to extract the finger-vein shape and orientation features is proposed. Secondly, to accommodate the potential local and global variations at the same time, a region-based matching scheme is investigated by employing the Scale Invariant Feature Transform (SIFT) matching method. Finally, the finger-vein shape, orientation and SIFT features are combined to further enhance the performance. The experimental results on databases of 426 and 170 fingers demonstrate the consistent superiority of the proposed approach.

Finger-vein recognition has received increased attention recently. However, the finger-vein images are always captured in poor quality. This certainly makes finger-vein feature representation unreliable, and further impairs the accuracy of finger-vein recognition. In this paper, we first give an analysis of the intrinsic factors causing finger-vein image degradation, and then propose a simple but effective image restoration method based on scattering removal. To give a proper description of finger-vein image degradation, a biological optical model (BOM) specific to finger-vein imaging is proposed according to the principle of light propagation in biological tissues. Based on BOM, the light scattering component is sensibly estimated and properly removed for finger-vein image restoration. Finally, experimental results demonstrate that the proposed method is powerful in enhancing the finger-vein image contrast and in improving the finger-vein image matching accuracy.

Finger-vein recognition, which is one of the conventional biometrics, hinders fake attacks, is cheaper, and it features a higher level of user-convenience than other biometrics because it uses miniaturized devices. However, the recognition performance of finger-vein recognition methods may decrease due to a variety of factors, such as image misalignment that is caused by finger position changes during image acquisition or illumination variation caused by non-uniform near-infrared (NIR) light. To solve such problems, multimodal biometric systems that are able to simultaneously recognize both finger-veins and fingerprints have been researched. However, because the image-acquisition positions for finger-veins and fingerprints are different, not to mention that finger-vein images must be acquired in NIR light environments and fingerprints in visible light environments, either two sensors must be used, or the size of the image acquisition device must be enlarged. Hence, there are multimodal biometrics based on finger-veins and finger shapes. However, such methods recognize individuals that are based on handcrafted features, which present certain limitations in terms of performance improvement. To solve these problems, finger-vein and finger shape multimodal biometrics using near-infrared (NIR) light camera sensor based on a deep convolutional neural network (CNN) are proposed in this research. Experimental results obtained using two types of open databases, the Shandong University homologous multi-modal traits (SDUMLA-HMT) and the Hong Kong Polytechnic University Finger Image Database (version 1), revealed that the proposed method in the present study features superior performance to the conventional methods.

Finger-vein verification is an emerging biometrics technology. Its first task is extracting finger-vein patterns. Although existing algorithms can extract most finger-vein patterns robustly, some branch of these patterns always breaks, which leads to adverse effects for features extraction and matching. In this paper, a Direction-Variance-Boundary Constraint Search (DVBCS) model is presented to restore the broken finger-vein patterns. At the beginning, endpoints of broken finger-vein branches are located. Then, a direction constraint for searching candidate point set is demonstrated. Following the second stage, an optimal target point is selected from the candidate point set according to a minimum within-cluster variance criterion. Eventually, the boundary constraint and variance constraint are introduced as the termination conditions. Experimental results illustrate that, while maintaining low segmentation error, the proposed method can restore above 10% lost target points. Moreover, the equal error rate of finger-vein recognition is reduced from 0.57% to 0.29% when using the proposed method to restore finger-vein patterns. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a new scheme to improve the performance of finger-vein identification systems. Firstly, a vein pattern extraction method to extract the finger-vein shape and orientation features is proposed. Secondly, to accommodate the potential local and global variations at the same time, a region-based matching scheme is investigated by employing the Scale Invariant Feature Transform (SIFT) matching method. Finally, the finger-vein shape, orientation and SIFT features are combined to further enhance the performance. The experimental results on databases of 426 and 170 fingers demonstrate the consistent superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

Image segmentation is an important step for finger-vein identification technique. However, it is difficult to extract precise details of the image because of the irregular noise and shades around the finger-vein. The repeated line tracking algorithm achieves good segmentation performance for low quality images of finger-vein, but it has some drawbacks such as low robustness and efficiency. In this paper, a modified repeated line tracking algorithm is proposed for image segmentation of finger-vein. Firstly, we propose a segmentation method called threshold image to execute rough segmentation and obtain binary and skeleton image of finger-vein. Secondly, the width of finger-vein is estimated based on the binary and skeleton image. The parameters are revised according to the width. Then, the modified repeated line tracking algorithm is executed to figure out the locus space of finger-vein based on the revised parameters. Finally, processing results are obtained by using Otsu algorithm which executes exact segmentation on the locus space. Experiments show that the proposed algorithm is more robust and efficient than traditional repeated line tracking algorithm. [ABSTRACT FROM AUTHOR]

Abstract: Finger–vein based biometrics, as a new approach to personal identification, has received much attention in recent years. However, the poor visibility of finger–vein imageries is really not beneficial for deepening the understanding of finger–vein characteristics. Moreover, unreliable finger–vein region of interest (ROI) localization can also heavily degrade the performance of a finger–vein based recognition system in practical scenario. Hence, in this paper, we first introduces a new and robust approach for finger–vein ROI localization, and then proposes a new scheme for effectively improving the visibility of finger–vein imageries. Extensive experiments are finally conducted to validate the proposed method. [Copyright &y& Elsevier]

Abstract: Multimodal biometrics based on feature-level fusion is a significant topic in personal identification research community. In this paper, a new fingerprint-vein based biometric method is proposed for making a finger more universal in biometrics. The fingerprint and finger-vein features are first exploited and extracted using a unified Gabor filter framework. Then, a novel supervised local-preserving canonical correlation analysis method (SLPCCAM) is proposed to generate fingerprint-vein feature vectors (FPVFVs) in feature-level fusion. Based on FPVFVs, the nearest neighborhood classifier is employed for personal identification finally. Experimental results show that the proposed approach has a high capability in fingerprint-vein based personal recognition as well as multimodal feature-level fusion. [Copyright &y& Elsevier]

Abstract: The finger-vein pattern is one of the human biometric signatures that can be used for personal verification. The first task of a verification process using finger-vein patterns is extracting the pattern from an infrared finger image. As a robust extraction method, we propose the mean curvature method, which views the vein image as a geometric shape and finds the valley-like structures with negative mean curvatures. When the matched pixel ratio is used in matching vein patterns, experimental results show that, while maintaining low complexity, the proposed method achieves 0.25% equal error rate, which is significantly lower than what existing methods can achieve. [Copyright &y& Elsevier]

With the development of consumer electronics technologies, many more powerful consumer devices such as cell phone, PDA and laptop have brought consumers an unprecedented level of convenience and flexibility. However, how to protect personal private information stored in the consumer electronics devices from misuses owing to theft or loss is becoming a major issue. To solve the problem, more secure and reliable user identification mechanisms using biometrics technology should be equipped into the consumer electronics devices. This paper presents a user identification system framework using finger-vein technology for consumer electronics devices. The finger-vein identification system is one of the biometrics sensor technologies, which provides high security and reliability than other identification technology. The algorithm of the proposed system compose a feature extraction using Radon transform and singular value decomposition (SVD) and classification using a normalized distance measure. The experimental results indicate that the proposed system performs well for user identification and achieves good performance in terms of the false acceptance rate (FAR) and the false rejection rate (FRR). [ABSTRACT FROM AUTHOR]

Acne is a skin issue that plagues many young people and adults. Even if it is cured, it leaves acne spots or acne scars, which drives many individuals to use skincare products or undertake medical treatment. On the contrary, the use of inappropriate skincare products can exacerbate the condition of the skin. In view of this, this work proposes the use of computer vision (CV) technology to realize a new business model of facial skincare products. The overall framework is composed of a finger vein identification system, skincare products' recommendation system, and electronic payment system. A finger vein identification system is used as identity verification and personalized service. A skincare products' recommendation system provides consumers with professional skin analysis through skin type classification and acne detection to recommend skincare products that finally improve skin issues of consumers. An electronic payment system provides a variety of checkout methods, and the system will check out by finger-vein connections according to membership information. Experimental results showed that the equal error rate (EER) comparison of the FV-USM public database on the finger-vein system was the lowest and the response time was the shortest. Additionally, the comparison of the skin type classification accuracy was the highest. [ABSTRACT FROM AUTHOR]

• The proposed system is robust to finger rotation. • The proposed system is a low-cost, user-friendly, and portable system. • The proposed system can acquire all fingerprint, finger-vein, finger-knuckle images. • Experimental results are promising and validate the usefulness of our system. Authentication systems play an essential role in our lives today. Human biological, behavioral, and morphological characteristics are usually used in an authentication system in a vast scope of applications ranging from unlocking consumer devices to surveillance and forensic analysis and offered an alternative to credit cards, ID cards, passports, driving licenses, etc. Unibiometric systems, which use a single biometric modality, suffer from some drawbacks such as low protection of user's privacy against attacks. Multibiometric systems, which fuse features of biometric characteristics, can cope with unibiometric systems' drawbacks and improve security and recognition accuracy. However, some main questions such as 'what is the optimal number of biometric modalities?', 'how much accuracy do we need for our system?', and 'how much money do we want to invest for our system?' have to be considered and answered before designing and implementing a multibiometric system. Unfortunately, the existing multibiometric systems have not considered and responded to all questions. Furthermore, identification mode for multibiometric systems is a challenging task and almost all of them have focused on verification mode. In this paper, we only consider a finger and employ the maximum possible modalities of the finger, i.e., fingerprint, finger-vein, and finger-knuckle-print, to increase user-friendliness and reducing the cost of implementation of the system. The proposed system only uses three cameras to capture all contactless fingerprint, finger-vein, and finger-knuckle images. Also, we propose an algorithm that makes enable the system to work in identification mode. The experiments on established databases exhibit that our proposed algorithm significantly increases recognition accuracy. [ABSTRACT FROM AUTHOR]

Region of interest (ROI) localization is one of the key preprocessing technologies for a finger-vein identification system, so an effective ROI definition can improve the matching accuracy. However, due to the impact of uneven illumination, equipment noise, as well as the distortion of finger position, etc., these make accurate ROI localization a very difficult task. To address these issues, in this paper, we propose a robust finger-vein ROI localization method, which is based on the 3 σ criterion dynamic threshold strategy. The proposed method includes three main steps: First, the Kirsch edge detector is introduced to detect the horizontal-like edges in the acquired finger-vein image. Then, the obtained edge gradient image is divided into four parts: upper-left, upper-right, lower-left, and lower-right. For each part of the image, the three-level dynamic threshold, which is based on the 3 σ criterion of the normal distribution, is imposed to obtain more distinct and complete edge information. Finally, through labeling the longest connected component at the same horizontal line, two reliable finger boundaries, which represent the upper and lower boundaries, respectively, are defined, and the ROI is localized in the region between these two boundaries. Extensive experiments are carried out on four different finger-vein image datasets, including three publicly available datasets and one of our newly developed finger-vein datasets with 37,080 finger-vein samples and 1030 individuals. The experimental results indicate that our proposed method has very competitive ROI localization performance, as well as satisfactory matching results on different datasets. [ABSTRACT FROM AUTHOR]

Today, biometrics is being widely used in various fields. Finger-vein is a type of biometric information and is based on finger-vein patterns unique to each individual. Various spoofing attacks have recently become a threat to biometric systems. A spoofing attack is defined as an unauthorized user attempting to deceive a system by presenting fake samples of registered biometric information. Generally, finger-vein recognition, using blood vessel characteristics inside the skin, is known to be more difficult when producing counterfeit samples than other biometrics, but several spoofing attacks have still been reported. To prevent spoofing attacks, conventional finger-vein recognition systems mainly use the difference in texture information between real and fake images, but such information may appear different depending on the camera. Therefore, we propose a method that can detect forged finger-vein independently of a camera by using remote photoplethysmography. Our main idea is to get the vital sign of arterial blood flow, a biometric measure indicating life. In this paper, we selected the frequency spectrum of time domain signal obtained from a video, as the feature, and then classified data as real or fake using the support vector machine classifier. Consequently, the accuracy of the experimental result was about 96.46%. [ABSTRACT FROM AUTHOR]

Finger-vein recognition, which is one of the conventional biometrics, hinders fake attacks, is cheaper, and it features a higher level of user-convenience than other biometrics because it uses miniaturized devices. However, the recognition performance of finger-vein recognition methods may decrease due to a variety of factors, such as image misalignment that is caused by finger position changes during image acquisition or illumination variation caused by non-uniform near-infrared (NIR) light. To solve such problems, multimodal biometric systems that are able to simultaneously recognize both finger-veins and fingerprints have been researched. However, because the image-acquisition positions for finger-veins and fingerprints are different, not to mention that finger-vein images must be acquired in NIR light environments and fingerprints in visible light environments, either two sensors must be used, or the size of the image acquisition device must be enlarged. Hence, there are multimodal biometrics based on finger-veins and finger shapes. However, such methods recognize individuals that are based on handcrafted features, which present certain limitations in terms of performance improvement. To solve these problems, finger-vein and finger shape multimodal biometrics using near-infrared (NIR) light camera sensor based on a deep convolutional neural network (CNN) are proposed in this research. Experimental results obtained using two types of open databases, the Shandong University homologous multi-modal traits (SDUMLA-HMT) and the Hong Kong Polytechnic University Finger Image Database (version 1), revealed that the proposed method in the present study features superior performance to the conventional methods. [ABSTRACT FROM AUTHOR]

Finger-vein recognition has received increased attention recently. However, the finger-vein images are always captured in poor quality. This certainly makes finger-vein feature representation unreliable, and further impairs the accuracy of finger-vein recognition. In this paper, we first give an analysis of the intrinsic factors causing finger-vein image degradation, and then propose a simple but effective image restoration method based on scattering removal. To give a proper description of finger-vein image degradation, a biological optical model (BOM) specific to finger-vein imaging is proposed according to the principle of light propagation in biological tissues. Based on BOM, the light scattering component is sensibly estimated and properly removed for finger-vein image restoration. Finally, experimental results demonstrate that the proposed method is powerful in enhancing the finger-vein image contrast and in improving the finger-vein image matching accuracy.