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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]

• We propose a method of generating multiple views by using single lead signal. • We introduce the sample diversity information into multi-view learning to seek the more effective discriminant subspace. • To reduce the influence of noise, we introduce a constraint to adaptively learn the relationships between different views. Currently, electrocardiogram (ECG) biometric recognition is a novel research trend, and many methods have been developed. Due to the influence of physical and psychological activities, there are heartbeats diversities of the same person. However, the existing ECG biometric recognition methods do not make use of sample diversity information. In this paper, we present a multi-view discriminant analysis approach in the consideration of sample diversity for ECG biometric recognition. Firstly, we propose a method of generating multiple views by using single lead ECG signal. Secondly, we present a multi-views learning framework, which takes sample diversity into account to generate a more discriminative subspace. Thirdly, to obtain a more robust solution, we introduce a denoising constraint to learn the relationships between different views, which can create a stable representation against ECG noise. At last, experimental results demonstrate that compared with the state-of-the-art methods on four databases, the proposed method can achieve competitive performance compared to state-of-the-art ECG biometric recognition methods. [ABSTRACT FROM AUTHOR]

• GNMF method is used to integrate the ECG data information, geometry and label information. • GNMF also achieves dimensionality reduction and primary feature extraction. • Sparse representation is utilized to obtain final features to perform ECG matching. • Our method shows robust recognition performance on two public datasets. As a vital sign, Electrocardiogram (ECG) has highly discriminative characteristics in the field of biometrics. This paper aims to propose a novel robust ECG biometric method based on graph regularized non-negative matrix factorization (GNMF) and sparse representation. First, after ECG signal pre-processing and heartbeat segmentation, GNMF is used to reduce the dimensions of each heartbeat. In GNMF, an affinity graph is constructed to encode the geometrical information and label information in order to obtain more discriminative features. Second, in order to seek highly discriminability of ECG, the sparse representation is utilized to perform final feature extraction. We evaluate the method on two public datasets: ECG-ID and MIT-BIH Arrhythmia (MITDB). When fusing three heartbeats as a test sample, the accuracy achieves 98.03% and 100% on the ECG-ID dataset and the MITDB dataset, respectively. Experimental results show that the proposed method is robust for within-session and across-session of the ECG signal. [ABSTRACT FROM AUTHOR]

Recently, finger vein recognition has received considerable attention in the biometric recognition field. Originating from fingerprint recognition, minutiae-based methods are recognized as an important branch, which attempts to discover minutia patterns from finger vein images for matching and recognition. However, the accuracy of these methods is generally unsatisfactory. One of the most challenging problems is that, the correspondences of two minutia sets are difficult to obtain resulting from the rotation, translation and deformation of the finger vein images. Another critical problem is that, the current available feature descriptors for minutia representation are weak and insufficient. In this paper, we propose SVDMM, a singular value decomposition (SVD)-based minutiae matching method for finger vein recognition, which involves three stages: (I) minutia pairing, (II) false removing and (III) score calculating. In particular, stage I discovers minutia pairs via SVD-based decomposition of the correlation-weighted proximity matrix. Stage II removes false pairs based on the local extensive binary pattern (LEBP) for increasing the reliability of the correspondences. Stage III determines the matching score of the input and template images by the ‘average’ matching degree of all their precise minutia pairs. Extensive experiments demonstrate that our work not only performs better than the similar works in the literature, but also has great potential to achieve comparable performance to other categories of state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Abstract: Soft biometric trait has been used as ancillary information to enhance the recognition accuracy for face, fingerprint, gait, iris, etc. In this paper, we present a new investigation of soft biometric trait to improve the performance of finger vein recognition. We first propose some extraction criteria of soft biometric trait for comprehensively understanding this kind of ancillary information. And then based on these criteria, the width of phalangeal joint is employed as a novel soft biometric trait, which can be directly extracted from finger vein image. Finally, three frameworks are developed to conduct the combination of the width measurement and finger vein pattern, i.e., the fusion framework, the filter framework and the hybrid framework. We perform rigorous experiments both on the open and self-built finger vein databases, and experimental results illustrate that soft biometric trait can make promising improvement of finger vein recognition performance. [Copyright &y& Elsevier]

• We propose a robust multi-feature collective non-negative matrix factorization model. • We learn an unified representation in the semantic space from multiple LBP histograms. • We integrate label information and multiple norms to enhance the discrimination. The field of electrocardiogram (ECG) biometrics has received considerable attention in recent years. Although some promising methods have been proposed, it is challenging to design a robust and precise method to improve the recognition performance of ECG signals with noise and sample variation. While the advantage of improved local binary pattern (LBP) for establishing identities has been widely recognized, extracting the latent semantics from multiple LBP features has attracted little attention. We propose a robust multi-feature collective non-negative matrix factorization (RMCNMF) model to handle noise and sample variation in ECG Biometrics. We extract multiple LBP histograms as feature descriptors from segmented ECG signals, and propose a multi-feature learning framework that learns unified representations in the shared latent semantic space via collective non-negative matrix factorization. To further enhance the discrimination of learned representations, we integrate label information and multiple norms in the proposed model, which not only preserves intra- and inter-subject similarities but also mitigates the influence of noise and sample variation. RMCNMF can be solved by an efficient iteration method, for which we provide a convergence analysis in detail. Extensive experiments on four ECG databases show that it performs competitively with state-of-the-art methods. [ABSTRACT FROM AUTHOR]