Local feature descriptors exhibit great superiority in finger vein recognition due to their stability and robustness against local changes in images. However, most of these are methods use general-purpose descriptors that do not consider finger vein-specific features. In this work, we propose a finger vein-specific local feature descriptors based physiological characteristic of finger vein patterns, i.e., histogram of oriented physiological Gabor responses (HOPGR), for finger vein recognition. First, prior of directional characteristic of finger vein patterns is obtained in an unsupervised manner. Then the physiological Gabor filter banks are set up based on the prior information to extract the physiological responses and orientation. Finally, to make feature has robustness against local changes in images, histogram is generated as output by dividing the image into non-overlapping cells and overlapping blocks. Extensive experimental results on several databases clearly demonstrate that the proposed method outperforms most current state-of-the-art finger vein recognition methods.
Hunan First Normal University（湖南第一师范学院）；National University of Defense Technology（国防科技大学）；Northeastern University（东北大学）
Finger vein recognition is an emerging biometric recognition technology. Different from the other biometric features on the body surface, the venous vascular tissue of the fingers is buried deep inside the skin. Due to this advantage, finger vein recognition is highly stable and private. They are almost impossible to be stolen and difficult to interfere with by external conditions. Unlike the finger vein recognition methods based on traditional machine learning, the artificial neural network technique, especially deep learning, it without relying on feature engineering and have superior perfor…
Peking University（北京大学）；Shenzhen Maidi Technology Co., LTD.（深圳迈迪科技有限公司）；TigerIT（TigerIT公司）；Chittagong University of Engineering & Technology（吉大港工程技术大学）
In recent years, finger vein recognition has become an important sub-field in biometrics and been applied to real-world applications. The development of finger vein recognition algorithms heavily depends on large-scale real-world data sets. In order to motivate research on finger vein recognition, we released the largest finger vein data set up to now and hold finger vein recognition competitions based on our data set every year. In 2017, International Competition on Finger Vein Recognition(ICFVR) is held jointly with IJCB 2017. 11 teams registered and 10 of them joined the final evaluation. T…
Identifying the origin of a sample image in biometric systems can be beneficial for data authentication in case of attacks against the system and for initiating sensor-specific processing pipelines in sensor-heterogeneous environments. Motivated by shortcomings of the photo response non-uniformity (PRNU) based method in the biometric context, we use a texture classification approach to detect the origin of finger vein sample images. Based on eight publicly available finger vein datasets and applying eight classical yet simple texture descriptors and SVM classification, we demonstrate excellent…
Malek-Ashtar University of Technology（马立克阿什塔尔理工大学）；Semnan University（塞姆南大学）；Iran University of Science and technology（伊朗科技大学）
In this paper, we propose a method for user Finger Vein Authentication (FVA) as a biometric system. Using the discriminative features for classifying theses finger veins is one of the main tips that make difference in related works, Thus we propose to learn a set of representative features, based on autoencoders. We model the user finger vein using a Gaussian distribution. Experimental results show that our algorithm perform like a state-of-the-art on SDUMLA-HMT benchmark.
Most finger vein feature extraction algorithms achieve satisfactory performance due to their texture representation abilities, despite simultaneously ignoring the intensity distribution that is formed by the finger tissue, and in some cases, processing it as background noise. In this paper, we exploit this kind of noise as a novel soft biometric trait for achieving better finger vein recognition performance. First, a detailed analysis of the finger vein imaging principle and the characteristics of the image are presented to show that the intensity distribution that is formed by the finger tiss…
FingerVeinSyn-5M: A Million-Scale Dataset and Benchmark for Finger Vein Recognition
School of Cyber Science and Engineering, Southeast University（网络安全科学与工程学院，东南大学）；Lee Kong Chian School of Medicine, Nanyang, Technological University Singapore（李光前医学院，南洋理工大学新加坡）；Institute of Automation, Chinese Academy of Sciences（自动化研究所，中国科学院）；Center for Machine Vision and Signal Analysis , University of Oulu（机器视觉与信号分析中心，奥卢大学）
CFVNet: An End-to-End Cancelable Finger Vein Network for Recognition
School of Cyber Science and Engineering, Southeast University（东南大学网络空间科学与工程学院）；Purple Mountain Laboratories（紫金山实验室）；University of Macao（澳门大学）；The Hong Kong University of Science and Technology（香港科技大学）
Nanjing University of Posts and Telecommunications（南京邮电大学）
Due to the instability and limitations of unimodal biometric systems, multimodal systems have attracted more and more attention from researchers. However, how to exploit the independent and complementary information between different modalities remains a key and challenging problem. In this paper, we propose a multimodal biometric fusion recognition algorithm based on fingerprints and finger veins (Fingerprint Finger Veins-Channel Spatial Attention Fusion Module, FPV-CSAFM). Specifically, for each pair of fingerprint and finger vein images, we first propose a simple and effective Convolutional…
Hunan First Normal University（湖南第一师范学院）；National University of Defense Technology（国防科技大学）；Northeastern University（东北大学）
Finger vein image recognition technology plays an important role in biometric recognition and has been successfully applied in many fields. Because veins are buried beneath the skin tissue, finger vein image recognition has an unparalleled advantage, which is not easily disturbed by external factors. This review summarizes 46 papers about deep learning for finger vein image recognition from 2017 to 2021. These papers are summarized according to the tasks of deep neural networks. Besides, we present the challenges and potential development directions of finger vein image recognition.
University of Science, VNU-HCM（越南胡志明市国家大学科学大学）；Vietnam National University, Ho Chi Minh City（越南胡志明市国家大学）；National Institute of Informatics（日本国立信息学研究所）；University of Tokyo（东京大学）
Finger vein recognition (FVR) systems have been commercially used, especially in ATMs, for customer verification. Thus, it is essential to measure their robustness against various attack methods, especially when a hand-crafted FVR system is used without any countermeasure methods. In this paper, we are the first in the literature to introduce master vein attacks in which we craft a vein-looking image so that it can falsely match with as many identities as possible by the FVR systems. We present two methods for generating master veins for use in attacking these systems. The first uses an adapta…
Convolutional neural networks have become a popular research in the field of finger vein recognition because of their powerful image feature representation. However, most researchers focus on improving the performance of the network by increasing the CNN depth and width, which often requires high computational effort. Moreover, we can notice that not only the importance of pixels in different channels is different, but also the importance of pixels in different positions of the same channel is different. To reduce the computational effort and to take into account the different importance of pi…
Finger vein verification has developed a lot since its first proposal, but there is still not a perfect algorithm. It is proved that algorithms with the same overall accuracy may have different misclassified patterns. We could make use of this complementation to fuse individual algorithms together for more precise result. According to our observation, algorithm has different confidence on its decisions but it is seldom considered in fusion methods. Our work is first to define decision reliability ratio to quantify this confidence, and then propose the Maximum Decision Reliability Ratio (MDRR) …
We study the finger vein (FV) sensor model identification task using a deep learning approach. So far, for this biometric modality, only correlation-based PRNU and texture descriptor-based methods have been applied. We employ five prominent CNN architectures covering a wide range of CNN family models, including VGG16, ResNet, and the Xception model. In addition, a novel architecture termed FV2021 is proposed in this work, which excels by its compactness and a low number of parameters to be trained. Original samples, as well as the region of interest data from eight publicly accessible FV datas…
Today, finger vein identification is gaining popularity as a potential biometric identification framework solution. Machine learning-based unsupervised, supervised, and deep learning algorithms have had a significant influence on finger vein detection and recognition at the moment. Deep learning, on the other hand, necessitates a large number of training datasets that must be manually produced and labeled. In this research, we offer a completely automated unsupervised learning strategy for training dataset creation. Our method is intended to extract and build a decent binary mask training data…
Finger vein recognition has drawn increasing attention as one of the most popular and promising biometrics due to its high distinguishes ability, security and non-invasive procedure. The main idea of traditional schemes is to directly extract features from finger vein images or patterns and then compare features to find the best match. However, the features extracted from images contain much redundant data, while the features extracted from patterns are greatly influenced by image segmentation methods. To tack these problems, this paper proposes a new finger vein recognition by generating code…
Despite the high biometric performance, finger-vein recognition systems are vulnerable to presentation attacks (aka., spoofing attacks). In this paper, we present a new and robust approach for detecting presentation attacks on finger-vein biometric systems exploiting the 3D Shape (normal-map) and material properties (diffuse-map) of the finger. Observing the normal-map and diffuse-map exhibiting enhanced textural differences in comparison with the original finger-vein image, especially in the presence of varying illumination intensity, we propose to employ textural feature-descriptors on both …
The last decade has brought forward many great contributions regarding presentation attack detection for the domain of finger and hand vein biometrics. Among those contributions, one is able to find a variety of different attack databases that are either private or made publicly available to the research community. However, it is not always shown whether the used attack samples hold the capability to actually deceive a realistic vein recognition system. Inspired by previous works, this study provides a systematic threat evaluation including three publicly available finger vein attack databases…
School of Cyber Science and Engineering, Southeast University（东南大学网络空间科学与工程学院）；Engineering Research Center of Blockchain Application, Supervision and Management (Southeast University), Ministry of Education（教育部区块链应用与监管管理工程研究中心（东南大学））；Purple Mountain Laboratories（紫金山实验室）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；Lee Kong Chian School of Medicine, Nanyang Technological University（南洋理工大学李光前医学院）；Faculty of Engineering, Shenzhen MSU-BIT University（深圳北理莫斯科大学工程学院）；VinUniversity；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Many deep learning-based models have been introduced in finger vein recognition in recent years. These solutions, however, suffer from data dependency and are difficult to achieve model generalization. To address this problem, we are inspired by the idea of domain adaptation and propose a universal learning-based framework, which achieves generalization while training with limited data. To reduce differences between data distributions, a compressed U-Net is introduced as a domain mapper to map the raw region of interest image onto a target domain. The concentrated target domain is a unified fe…
AGVBench: A Reliability-Oriented Benchmark of Data Augmentation for Vein Recognition
Chongqing Technology and Business University（重庆理工大学）；School of Engineering, Westlake University（西湖大学工程学院）；SAMOVAR, Telecom SudParis, Institute Polytechnique de Paris（SAMOVAR，Telecom SudParis，巴黎高等理工学院）；Guangzhou College of Applied Science and Technology（广州应用科学与技术学院）
Adversarial Masking Contrastive Learning for vein recognition
Chongqing Technology and Business University（重庆工商大学）；Telecom SudParis（巴黎电信学院）；University Paris-Saclay（巴黎-萨克雷大学）；China University of Mining and Technology（中国矿业大学）
A central challenge in building robotic prostheses is the creation of a sensor-based system able to read physiological signals from the lower limb and instruct a robotic hand to perform various tasks. Existing systems typically perform discrete gestures such as pointing or grasping, by employing electromyography (EMG) or ultrasound (US) technologies to analyze muscle states. While estimating finger gestures has been done in the past by detecting prominent gestures, we are interested in detection, or inference, done in the context of fine motions that evolve over time. Examples include motions …
Biometrics which use of human physiological characteristics for identifying an individual is now a widespread method of identification and authentication. Biometric identification is a technology which uses several image processing techniques and describes the general procedure for identification and verification using feature extraction, storage and matching from the digitized image of biometric characters such as Finger Print, Face, Iris or Palm Print. The current paper uses palm print biometrics. Here we have presented an identification approach using textural properties of palm print image…
Dr. B. C. Roy Engineering College（Dr. B. C. Roy工程学院）；National Institute of Technology Rourkela（鲁尔克拉国家理工学院）；Indian Institute of Technology Kanpur（坎普尔印度理工学院）；Jadavpur University（jadavpur大学）
Ear biometric is considered as one of the most reliable and invariant biometrics characteristics in line with iris and fingerprint characteristics. In many cases, ear biometrics can be compared with face biometrics regarding many physiological and texture characteristics. In this paper, a robust and efficient ear recognition system is presented, which uses Scale Invariant Feature Transform (SIFT) as feature descriptor for structural representation of ear images. In order to make it more robust to user authentication, only the regions having color probabilities in a certain ranges are considere…
