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Fusion of hand vein, iris and fingerprint for person identity verification based on Bayesian theory

Biometric identification is an important guarantee for social security. In recent years, as the development of social and economic, the more accuracy and safety of identification are required. The person identity verification systems that use a single biometric appear inherent limitations in accuracy, user acceptance, universality. Limitations of unimodal biometric systems can be overcome by using multimodal biometric systems, which combines the conclusions made by a number of unrelated biometrics indicators. Aiming at the limitations of unimodal biometric identification, a recognition algorithm for multimodal biometric fusion based on hand vein, iris and fingerprint was proposed. To verify person identity, the hand vein images, iris images and fingerprint images were preprocessed firstly. The region of interest (ROI) of hand vein image was obtained and filtered to reduce image noises. The multiresolution analysis theory was utilized to extract the texture information of hand vein. The iris image was preprocessed through iris localization, eyelid detection, image normalization and image enhancement, and then the feature code of iris was extracted from the detail images obtained using wavelet transform. The texture feature information represented fingerprint pattern was extracted after filtering and image enhancement. The Bayesian theorem was employed to realize the fusion at the matching score level and the fusion recognition result was finally obtained. The experimental results were presented, which showed that the recognition performance of the proposed fusion method was obviously higher than that of single biometric recognition algorithm. It had verified the efficiency of the proposed method for biometrics.