A Multimodal Biometric System using Iris and Palmprint

Abstract

A biometric system is basically a system of image recognition that uses bio metric characteristics to identify individuals. The thesis introduces a biometric multimodal system that is based on iris-based Palm Print verification and fusion. We suggest an approach to extracting features from each modality using four-level decomposition of the wavelet packet. It includes 256 packets capable of generating a simple binary code. Dictate standardized thresholds based on the first three highest energy peaks that would impact 0 or 1 for each wavelet packet. Specific fusion approaches were evaluated at different levels: character level, score level and error level. Its first fusion is an iris and palm print application, actually. For matching ratings the next one uses a weighted sum law. The next applies to the Hamacher t-norm's deficiencies. The standard database is used for testing the program proposed. The current approach and then each fusion method was checked for The consistency about the database of Casia iris merged with the database of Casia palm print. With each fusion process, the proposed solution to the multimodal biometric system achieves an increase in identification.