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.

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-theart 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.

Retinal identification based on retinal vasculatures in the retina provides the most secure and accurate means of authentication among biometrics and has primarily been used in combination with access control systems at high security facilities. Recently, there has been much interest in retina identification. As digital retina images always suffer from deformations, the Scale Invariant Feature Transform (SIFT), which is known for its distinctiveness and invariance for scale and rotation, has been introduced to retinal based identification. However, some shortcomings like the difficulty of feature extraction and mismatching exist in SIFT-based identification. To solve these problems, a novel preprocessing method based on the Improved Circular Gabor Transform (ICGF) is proposed. After further processing by the iterated spatial anisotropic smooth method, the number of uninformative SIFT keypoints is decreased dramatically. Tested on the VARIA and eight simulated retina databases combining rotation and scaling, the developed method presents promising results and shows robustness to rotations and scale changes.

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.