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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. The winner of this year dramatically improved the EER from 2.64% to 0.483% compared to the winner of last year. In this paper, we introduce the process and results of ICFVR 2017 and give insights on development of state-of-art finger vein recognition algorithms., 8 pages, 15 figures

Unimodal biometric verification has developed a lot and become more accurate, but there is still not a perfect algorithm. In the meantime, cases exist where unimodal verification system could not meet the requirements in practical use. 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 this is seldom considered in fusion methods. Our work focuses on this confidence. We first define decision reliability ratio to quantify this confidence, and then propose the Maximum Decision Reliability Ratio (MDRR) fusion scheme incorporating Weighted Voting. Two experiments conducted on different datasets prove the effectiveness of the method. One is to fuse 4 finger vein verification algorithms on a set of 1000 fingers and 5 images per finger. The other experiment fuse the multimodal set in NIST-BSSR1. Experiment results show the fusion method could largely improve verification accuracy, from 91.29% to 99.81%. It also shows the MDRR outperforms the commonly used fusion methods as Voting, Weighted Voting, Weighted Sum or even the theoretically optimal method Likelihood Ratio-based fusion.

Finger vein verification is using vein patterns to verify a person's identity, which is widely used in various fields. In practice, the method for verification is the most important part of a biometric system, which determines the reliability of the system. In this paper, we propose methods called DeepVein for finger vein verification based on deep convolutional neural networks and conduct experiments to evaluate our methods. The experimental results show that our proposed methods can achieve state-of-the-art performance in accuracy. In addition, we present how the amount of data for training affects the accuracy in the test datasets.

Finger vein recognition is a biometric method utilizing the vein patterns inside one's fingers for personal identification. Recognition algorithm is the key part of a finger vein recognition system, dominating the system performance. There are usually a lot of parameters in algorithms, and different values of the parameters could lead to different system performance so that it is essential to set a proper value for each parameter in practice. In this paper, we conduct a set of experiments to study how the parameters influence the performance measured by equal error rate. We have made two observations from the results: 1.When an algorithm is applied on a dataset, the performance differs a lot as the parameter value changes even in a small range; 2.When an algorithm is applied on different datasets, the performance differs a lot, in other words, the optimized parameter value combination that maximizes the system performance differs significantly. These two observations reveal the importance of parameter adjustment in finger vein recognition. So this paper proposes two solutions: search algorithm and estimation by subset, which are fast, accurate and scalable methods to find the best parameters. The experiment results prove the effectiveness of our methods.

Biometric features of humans, which include behavioral features and biological features, have two main popular application areas: 1. Biomedical application as in human disease diagnosis and health monitoring; 2. Security application as in identity authentication.

An indoor autonomous navigation system without GPS has been developed, based on the platform of quad-copter, which may fulfill the task of searching, identifying and entering the target room in a building with multi-rooms corridor. A pose sensor was utilized to stabilize the aircraft. The SLAM (Simultaneous Localization and Mapping) and plan route in unknown environment have been created by a 2D lidar. A calibrated monocular camera has been used to recognize different marks to make sure the vehicle to enter the target room. The test result showed that the indoor autonomous navigation technology based on lidar for quad-copter aerial robot is feasible and successful.

Finger vein recognition is a newly developed and promising biometrics technology. In current researches, finger vein recognition algorithms are mostly evaluated on data collected in laboratory environment. Along with its development, this technology gradually transforms from laboratory to actual use. Finger vein images captured in operational systems are different from those obtained in the lab, due to its uncontrollable acquisition condition and users' behavior. The 2nd Finger Vein Recognition Competition (FVRC2016) was organized for the purpose of assessing and comparing finger vein recognition algorithms on such data. 3 new data sets collected in real application were prepared for evaluating and improving algorithms. 6 submitted algorithms were extensively tested on the data sets. Testing protocols and detailed analysis of the results are also presented in this paper.

Biometric recognition requires that the biometric characteristics used for the verification should be unique among individuals. The purpose of this study is to verify the individuality of finger vein, a new trait with superiority on accuracy and security. To carry out the research, we construct a large-scale finger vein database consisting of 710,399 images from 363,703 fingers. We adopt a score level fusion strategy to reduce the negative impact of algorithm deficiencies. We also design a distributed computing system for more than 83 billion impostor comparisons. The experimental results demonstrate that finger vein is sufficiently unique to distinguish one person from another in such scale.

Biometric menagerie is an important phenomenon in biometric systems, which focuses on distinguishing the minority of people who perform poorly and cause the majority of the errors (FAR and FRR). It can help to evaluate biometric systems and improve their performance by analyzing the animal like users. The fundamental step of this theory is the detection of animals. If the detection is not accurate, it may lead to potential problems. However, the current theories carried out by Doddington et al. (1998) and Yager (2008) both neglected the threshold in biometric systems when detecting animals, which might reduce the accuracy of animal detection. To verify this conjecture, we apply the above two theories to detect the existence of animals on a special finger vein database PFVD - Perfect Finger Vein Database. The characteristic of PFVD is that its accuracy is 100%, indicating zero FAR and zero FRR. From the intuitive point of view, there should exist no goat, lamb or wolf in Doddington's menagerie, and no worm, chameleon or phantom in Yager's menagerie. However, the experiments show the negative results, implying that the current theories are not perfect on animal detection. This paper analyzes the two theories and proposes BMDT - Biometric Menagerie Detection with Threshold, an optimized method based on Yager. By taking threshold into account, BMDT makes a significant improvement on the accuracy of animal detection, compared to the current theory. We apply BMDT on PFVD, and the results show that the falsely detected animals by Yager's method are removed. In addition, we evaluate BMDT in 3 more general cases, proving the advantage of the proposed method.