Finger vein recognition
About: Finger vein recognition is a research topic. Over the lifetime, 800 publications have been published within this topic receiving 10670 citations.
Papers published on a yearly basis
01 Oct 2004
TL;DR: Experimental results show that the proposed method achieves robust pattern extraction, and the equal error rate was 0.145% in personal identification.
Abstract: We propose a method of personal identification based on finger-vein patterns. An image of a finger captured under infrared light contains not only the vein pattern but also irregular shading produced by the various thicknesses of the finger bones and muscles. The proposed method extracts the finger-vein pattern from the unclear image by using line tracking that starts from various positions. Experimental results show that it achieves robust pattern extraction, and the equal error rate was 0.145% in personal identification.
TL;DR: A new approach to improve the performance of finger-vein identification systems presented in the literature is presented and two new score-level combinations are developed and investigated, i.e., holistic and nonlinear fusion.
Abstract: This paper presents a new approach to improve the performance of finger-vein identification systems presented in the literature. The proposed system simultaneously acquires the finger-vein and low-resolution fingerprint images and combines these two evidences using a novel score-level combination strategy. We examine the previously proposed finger-vein identification approaches and develop a new approach that illustrates it superiority over prior published efforts. The utility of low-resolution fingerprint images acquired from a webcam is examined to ascertain the matching performance from such images. We develop and investigate two new score-level combinations, i.e., holistic and nonlinear fusion, and comparatively evaluate them with more popular score-level fusion approaches to ascertain their effectiveness in the proposed system. The rigorous experimental results presented on the database of 6264 images from 156 subjects illustrate significant improvement in the performance, i.e., both from the authentication and recognition experiments.
TL;DR: To robustly extract the precise details of the depicted veins, a method of calculating local maximum curvatures in cross-sectional profiles of a vein image is developed that can extract the centerlines of the veins consistently without being affected by the fluctuations in vein width and brightness.
Abstract: A biometrics system for identifying individuals using the pattern of veins in a finger was previously proposed. The system has the advantage of being resistant to forgery because the pattern is inside a finger. Infrared light is used to capture an image of a finger that shows the vein patterns, which have various widths and brightnesses that change temporally as a result of fluctuations in the amount of blood in the vein, depending on temperature, physical conditions, etc. To robustly extract the precise details of the depicted veins, we developed a method of calculating local maximum curvatures in cross-sectional profiles of a vein image. This method can extract the centerlines of the veins consistently without being affected by the fluctuations in vein width and brightness, so its pattern matching is highly accurate. Experimental results show that our method extracted patterns robustly when vein width and brightness fluctuated, and that the equal error rate for personal identification was 0.0009%, which is much better than that of conventional methods.
••03 Dec 2011
TL;DR: The acquisition and content of a new homologous multimodal biometric database are presented and the database is available to research community through http://mla.sdu.edu.cn/sdumla-hmt.
Abstract: In this paper, the acquisition and content of a new homologous multimodal biometric database are presented The SDUMLA-HMT database consists of face images from 7 view angles, finger vein images of 6 fingers, gait videos from 6 view angles, iris images from an iris sensor, and fingerprint images acquired with 5 different sensors The database includes real multimodal data from 106 individuals In addition to database description, we also present possible use of the database The database is available to research community through http://mlasdueducn/sdumla-hmthtml
TL;DR: Experimental results show that the proposed method using LLBP has better performance than the previous methods using LBP and local derivative pattern (LDP).
Abstract: In this paper, a personal verification method using finger vein is presented Finger vein can be considered more secured compared to other hands based biometric traits such as fingerprint and palm print because the features are inside the human body In the proposed method, a new texture descriptor called local line binary pattern (LLBP) is utilized as feature extraction technique The neighbourhood shape in LLBP is a straight line, unlike in local binary pattern (LBP) which is a square shape Experimental results show that the proposed method using LLBP has better performance than the previous methods using LBP and local derivative pattern (LDP)
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