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A biometrics system for identifying individuals using the pattern of veins in a finger

SUMMARYThe research group of which the authors are members has proposed a scheme based on finger vein patterns as a scheme of biometric identification utilizing biological information. Since the finger vein images taken to obtain finger vein patterns are obtained by irradiating the fingers with infrared rays, fluctuations in brightness due to variations in the light power or the thickness of the finger occur. This paper proposes a scheme for extracting global finger vein patterns by iteratively tracking local lines from various positions to robustly extract finger vein patterns from such unclear images. The proposed scheme is robust against brightness fluctuations as compared with the conventional feature extraction schemes and the results of evaluating the proposed scheme by applying it to a personal identification system showed an effective error rate of 0.145%.

A novel method for finger-vein authentication based on feature-point matching is proposed and evaluated. A finger-vein image captured by infrared light contains artifacts such as irregular shading and vein posture deformation that can degrade accuracy of finger-vein authentication. Therefore, a method is proposed for extracting features from vein patterns and for matching feature points that is robust against irregular shading and vein deformation. In the proposed method, curvature of image-intensity profiles is used for feature point extraction because such image profiles are a robust feature against irregular shading. To increase the number of feature points, these points are extracted from any positions where vein shape is non-linear. Moreover, a finger-shape model and non-rigid registration method are proposed. Both the model and the registration method correct a deformation caused by the finger-posture change. It is experimentally shown that the proposed method achieves more robust matching than conventional methods. Furthermore, experiments on finger-vein identification show that the proposed method provides higher identification accuracy than conventional methods.

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