Finger-vein biometrics have been extensively investigated for person verification. One of the open issues in finger-vein verification is the lack of robustness against variations of vein patterns due to the changes in physiological and imaging conditions during the acquisition process, which results in large intra-class variations among the finger-vein images captured from the same finger and may degrade the system performance. Despite recent advances in biometric template generation and improvement, current solutions mainly focus on the extrinsic biometrics (e.g., fingerprints, face, signature) instead of intrinsic biometrics (e.g., vein). This paper proposes a weighted least square regression based model to generate and improve enrollment template for finger-vein verification. Driven by the primary target of biometric template generation and improvement, i.e., verification error minimization, we assume that a good template has the smallest intra-class distance with respect to the images from the same class in a verification system. Based on this assumption, the finger-vein template generation is converted into an optimization problem. To improve the performance, the weights associated with similarity are computed for template generation. Then, the enrollment template is generated by solving the optimization problem. Subsequently, a template improvement model is proposed to gradually update vein features in the template. To the best of our knowledge, this is the first proposed work of template generation and improvement for finger-vein biometrics. The experimental results on two public finger-vein databases show that the proposed schemes minimize the intra-class variations among samples and significantly improve finger-vein recognition accuracy.
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