Gaussian directional pattern for dorsal hand vein recognition

Abstract

With the increasing needs in security systems, hand vein recognition is reliable as one of the important solutions for biometrics-based identification systems. This work presents an effective approach for dorsal hand vein recognition by analysing vein patterns. The methodology involves extraction of dorsal hand vein features based on Gaussian filter. In order to obtain effective pattern of dorsal hand vein vascular, we propose an innovative and robust Gaussian directional filter method to extract the dorsal hand vein patterns and to encode the vein features in binary code. A new coding scheme, namely Gaussian directional binary code, is then proposed for dorsal hand vein recognition. To evaluate the efficacy of the proposed approach, the normalised Hamming distance used in recognition is adopted. A total of 5120 dorsal hand vein images were collected from 256 persons to verify the validity of the proposed dorsal hand vein recognition approach. High accuracies (>99%) and low equal error rate (0·95%) have been obtained by the proposed method. Experimental results demonstrate that our proposed approach is feasible and effective for dorsal hand vein recognition.

Keywords:

• Biometrics
• Dorsal hand vein recognition
• Gaussian filter
• Hamming distance