Biometrics is widely used in life, and finger-vein recognition also has preliminary applications. Finger-vein recognition methods based on deep learning have achieved state-of-the-art performance, but there are still some challenges, such as sensitivity to finger posture, low recognition efficiency, etc. In our study, a data augmentation strategy using the random sliding window is designed to alleviate the problem caused by changes in finger posture. In addition, to improve the recognition efficiency while ensuring the accuracy, a lightweight finger-vein feature extraction model is proposed based on vision transformer, in which the tokens to token structure is able to describe the global correlation of images at multiple scales. Experimental results on three public databases indicate that compared with other deep-learning methods, the proposed method has achieved higher recognition efficiency and accuracy.
Resulting from nonuniform illumination and finger shift, current phalangeal joint locating methods cannot segment adequate vein pattern information and locate phalangeal joint reliably. Hence, we propose a robust gradient-based approach to detect phalangeal joint accurately and achieve sufficient valuable information. The detection accuracy is 98.23% and 99.32% in two datasets consisting of abnormal finger-vein images, respectively. Furthermore, the captured image contains noise and irregular shading inevitably, which can seriously affect further comprehensive study of finger-vein characteristics. Thus, we propose a fast and self-adaptive recovery model, which is built on atmospheric scattering model and finger-vein imaging principle, to extract finger-vein patterns reliably. Finally, a more reliable template matching method is used for finger-vein patterns matching. The resulting equal error rate is 2.95% and 2.83% in two databases, which consist of 1260 and 384 finger-vein images, respectively.
Finger vein recognition technology has significant advantages over other methods in terms of accuracy, uniqueness, and stability, and it has wide promising applications in the field of biometric recognition. We propose using finger creases to locate and extract an object region. Then we use linear fitting to overcome the problem of finger rotation in the plane. The method of modular adaptive histogram equalization (MAHE) is presented to enhance image contrast and reduce computational cost. To extract the finger vein features, we use a fusion method, which can obtain clear and distinguishable vein patterns under different conditions. We used the Hausdorff average distance algorithm to examine the recognition performance of the system. The experimental results demonstrate that MAHE can better balance the recognition accuracy and the expenditure of time compared with three other methods. Our resulting equal error rate throughout the total procedure was 3.268% in a database of 153 finger vein images.
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