Multimodal Biometric Person Authentication

This work present a multimodal biometric system. Speaker recognition system was built using Mel Frequency Cepstral Coefficients (MFCC) for feature extraction and Vector Quantization (VQ) for modeling. An offline signature recognition system was built using Discrete Cosine Transform (DCT) for feature extraction and Vertical and Horizontal Projection Profiles (VPP and HPP). The biometric person authentication technique based on the pattern of the human iris is well suited to be applied to any access control system requiring a high level of security. This paper examines a new iris recognition system that implements (i) gradient decomposed Hough transform / integro- differential operators' combination for iris localization and (ii) the "analytic image" concept (2D Hilbert transform) to extract pertinent information from iris texture. An off-line signature verification system based on fusion of two machine experts is presented. One of the experts is based on global image analysis and a statistical distance measure while the second one is based on local image analysis and Hidden Markov Models. Experimental results are given on a subcorpus of the large MCYT signature database for random and skilled forgeries.