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Finger veins are a promising biometric pattern for personalized identification in terms of their advantages over existing biometrics. Based on the spatial pyramid representation and the combination of more effective information such as gray, texture and shape, this paper proposes a simple but powerful feature, called Pyramid Histograms of Gray, Texture and Orientation Gradients (PHGTOG). For a finger vein image, PHGTOG can reflect the global spatial layout and local details of gray, texture and shape. To further improve the recognition performance and reduce the computational complexity, we select a personalized subset of features from PHGTOG for each subject by using the sparse weight vector, which is trained by using LASSO and called PFS-PHGTOG. We conduct extensive experiments to demonstrate the promise of the PHGTOG and PFS-PHGTOG, experimental results on our databases show that PHGTOG outperforms the other existing features. Moreover, PFS-PHGTOG can further boost the performance in comparison with PHGTOG.

Finger vein patterns are considered as one of the most promising biometric authentication methods for its security and convenience. Most of the current available finger vein recognition methods utilize features from a segmented blood vessel network. As an improperly segmented network may degrade the recognition accuracy, binary pattern based methods are proposed, such as Local Binary Pattern (LBP), Local Derivative Pattern (LDP) and Local Line Binary Pattern (LLBP). However, the rich directional information hidden in the finger vein pattern has not been fully exploited by the existing local patterns. Inspired by the Webber Local Descriptor (WLD), this paper represents a new direction based local descriptor called Local Directional Code (LDC) and applies it to finger vein recognition. In LDC, the local gradient orientation information is coded as an octonary decimal number. Experimental results show that the proposed method using LDC achieves better performance than methods using LLBP.

Finger vein patterns have recently been recognized as an effective biometric identifier. In this paper, we propose a finger vein recognition method based on a personalized best bit map (PBBM). Our method is rooted in a local binary pattern based method and then inclined to use the best bits only for matching. We first present the concept of PBBM and the generating algorithm. Then we propose the finger vein recognition framework, which consists of preprocessing, feature extraction, and matching. Finally, we design extensive experiments to evaluate the effectiveness of our proposal. Experimental results show that PBBM achieves not only better performance, but also high robustness and reliability. In addition, PBBM can be used as a general framework for binary pattern based recognition.

Finger vein recognition is a promising biometric technology that has received significant research attention. However, most of the existing works often relied on a single feature, which failed to fully exploit the discriminative information in finger vein images, and therefore led to a limited recognition performance. To overcome this limitation, this paper proposes an encoding coefficient similarity-based multifeature sparse representation method for finger vein recognition. The proposed method not only uses multiple features to extract comprehensive information from finger vein images, but also obtains more discriminative information through constraints in the objective function. The sparsity constraint retains the key information of each feature, and the similarity constraint explores the shared information among the features. Furthermore, the proposed method is capable of fusing all kinds of features, not limited to specific ones. The optimization problem of the proposed method is efficiently solved using the alternating direction multiplier method algorithm. Experimental results on two public finger vein databases HKPU-FV and SDU-FV show that the proposed method achieves good recognition performance. [ABSTRACT FROM AUTHOR]

Finger vein recognition is considered to be a very promising biometric identification technology due to its excellent recognition performance. However, in the real world, the finger vein recognition system inevitably suffers from the single-sample problem: that is, only one sample is registered per class. In this case, the performance of many classical finger vein recognition algorithms will decline or fail because they cannot learn enough intra-class variations. To solve this problem, in this paper, we propose a SIFT-flow-based virtual sample generation (SVSG) method. Specifically, first, on the generic set with multiple registered samples per class, the displacement matrix of each class is obtained using the scale-invariant feature transform flow (SIFT-flow) algorithm. Then, the key displacements of each displacement matrix are extracted to form a variation matrix. After removing noise displacements and redundant displacements, the final global variation matrix is obtained. On the single sample set, multiple virtual samples are generated for the single sample according to the global variation matrix. Experimental results on the public database show that this method can effectively improve the performance of single-sample finger vein recognition. [ABSTRACT FROM AUTHOR]

Vein pattern-based methods have powerfully promoted the performance of finger vein recognition. However, it is not easy to precisely extract vein patterns from images, especially from low-quality images, and the non-vein area have been proved to be helpful for recognition. This paper proposes to use low-rank representation to extract as much noiseless discriminative information as possible from finger vein images. However, image deformation and image quality variations weaken the correlation of genuine images, and therefore damage the low-rank linear representation. To further deal with this problem, the class labels of training images and the local geometric structure between testing images and training images, reflected by sparse reconstruction errors of testing images, are used as constraints of low-rank coefficients. In particular, vein backbone decomposition based sparse representation is proposed to fast compute the deformation-robust reconstruction errors of each testing image. The reconstruction error on sub-backbones of one training image are used and modified as the constraint of the low-rank coefficient on this training image. We evaluate the proposed method on three widely used finger vein databases, and experimental results show that the proposed method performs well in finger vein recognition. [ABSTRACT FROM AUTHOR]

This paper aims to develop a new local binary learning feature for finger vein images called personalized binary code (PBC). Existing local learning features of the image, such as compact binary face descriptor (CBFD) and discriminant binary descriptor (DBD), ignore the structure of the binary feature corresponding to each class when learning. Inspired by the bit-consistency phenomenon of local binary pattern (LBP), we combine the constraints of within-class sparse constraints with linear discriminant analysis (LDA) criteria in the learning process to explore the bit-consistent structure of learned binary features. Specifically, a multi-directional pixel difference vector is first calculated at each pixel of the training set of finger vein images, and an objective function is designed to learn a discriminative map that projects these multi-directional pixel difference vectors into binary features. Our objective function consists of several constraints: Minimizing quantization error, minimizing within-class variance of learned binary features, and maximizing the between-class variance of learned binary features, and minimizing the L 2,1 norm of the learned features for each class. Next, the algorithm based on alternating direction method (ADM) is designed to solve the optimization problem. Finally, a histogram representation of each finger vein image is constructed by the bag-of-words model. In our experiments, the proposed PBC shows that the leading performance on two public databases in comparison with existing finger vein recognition methods. [ABSTRACT FROM AUTHOR]

Vein pattern-based methods powerfully boost the recognition accuracy of finger veins, but real-time recognition cannot be guaranteed, especially in large-scale applications. Moreover, previous studies focused on either the matching task to enhance the accuracy or the indexing task to improve the efficiency. This paper proposes a finger vein code indexing method and combines it with a finger vein pattern matching method into an integration framework for improving both accuracy and efficiency. With the extracted vein patterns, the direction of each vein segment is detected and represented by the elliptical direction map as a feature for indexing, which will be encoded into a binary code by the angle K-means. The similarity between vein direction codes is measured by the grouped hamming distance in indexing, and further weighted by the overlap degree of the corresponding vein patterns to return the candidates for the probe. In addition, based on the above distance measurement, only vein segments with the same direction code are considered in following probe-to-candidate matching. Experimental results indicate that our indexing method outperforms the state-of-the-art methods and has competitive potential in performing the matching task. The results also indicate that the integration framework highly improves the identification efficiency with a slight improvement on the accuracy. [ABSTRACT FROM AUTHOR]

Abstract Biometric human identification refers to the automated recognition of individuals based on their biological or behavioral characteristics. Finger vein recognition and electrocardiogram (ECG) recognition have received a lot of attention for over ten years and these two traits are viewed as ones of promising biometric traits because of their unique advantages. However, these two independent unimodal biometrics, which using a single biometric trait for personal recognition, usually cannot satisfy the requirements of real-world applications. Finger vein and ECG signals have their unique advantages and also have their respective shortcomings. It has been well established that multimodal biometrics systems can overcome the respective defects of unimodal approach. Thus, how to jointly exploit these two modalities and develop an effective and efficient multimodal identification system are still open problems. However, to the best of our knowledge, there has been no study on the multimodal biometric systems using finger vein and ECG signals. Therefore, in this paper we make the first attempt to integrate finger vein with ECG signals for personal identification using various fusion strategies. A new multimodal biometric method based on Discriminant correlation analysis (DCA) for the fusion of finger vein and ECG is proposed. Extensive experiments are conducted on a merged bi-modal dataset named VeinECG coming from FVPolyU finger vein dataset and ECG-ID Dataset. Experimental results show that the proposed multimodal system is substantially superior to two individual unimodal systems in terms of both recognition accuracy and security. [ABSTRACT FROM AUTHOR]

Finger vein recognition has received a lot of attention recently and is viewed as a promising biometric trait. In related methods, vein pattern-based methods explore intrinsic finger vein recognition, but their performance remains unsatisfactory owing to defective vein networks and weak matching. One important reason may be the neglect of deep analysis of the vein anatomy structure. By comprehensively exploring the anatomy structure and imaging characteristic of vein patterns, this paper proposes a novel finger vein recognition framework, including an anatomy structure analysis-based vein extraction algorithm and an integration matching strategy. Specifically, the vein pattern is extracted from the orientation map-guided curvature based on the valley- or half valley-shaped cross-sectional profile. In addition, the extracted vein pattern is further thinned and refined to obtain a reliable vein network. In addition to the vein network, the relatively clear vein branches in the image are mined from the vein pattern, referred to as the vein backbone. In matching, the vein backbone is used in vein network calibration to overcome finger displacements. The similarity of two calibrated vein networks is measured by the proposed elastic matching and further recomputed by integrating the overlap degree of corresponding vein backbones. Extensive experiments on two public finger vein databases verify the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

In second paragraph of the footnote on the first page of , the institution information of Lu Yang and Xiaoming Xi is inaccurate. The correct institution name is “School of Computer Science and Technology, Shandong University of Finance and Economics.” So this paragraph should be corrected as: [ABSTRACT FROM AUTHOR]