Multimodal biometric recognition using iris feature extraction and palmprint features

A Biometric system is essentially a pattern recognition system that makes use of biometric traits to recognize individuals. Authentication systems built on only one biometric modality may not fulfill the requirements of demanding applications in terms of properties such as performance, acceptability and distinctiveness. Most of the unimodal biometrics systems have problems such as noise in collected data, intra-class variations, inter-class variations, non universality etc. Some of these limitations can be overcome by multiple source of information for establishing identity; such systems are known as multimodal biometric systems. In this paper a multi modal biometric system of iris and palm print based on Wavelet Packet Analysis is described. The most unique phenotypic feature visible in a person's face is the detailed texture of each eye's iris. Palm is the inner surface of a hand between the wrist and the fingers. Palmprint is referred to principal lines, wrinkles and ridges on the palm. The visible texture of a person's iris and palm print is encoded into a compact sequence of 2-D wavelet packet coefficients, which generate a “feature vector code”. In this paper, we propose a novel multi-resolution approach based on Wavelet Packet Transform (WPT) for texture analysis and recognition of iris and palmprint. The development of this approach is motivated by the observation that dominant frequencies of iris texture are located in the low and middle frequency channels. With an adaptive threshold, WPT sub images coefficients are quantized into 1, 0 or -1 as iris signature. This signature presents the local information of different irises. By using wavelet packets the size of the biometric signature of code attained is 960 bits. The signature of the new pattern is compared against the stored pattern after computing the signature of new input pattern. Identification is performed by computing the hamming distance.