Fingerprint and Iris Fusion for personal Identification


A variety of researches Dealt with the fusion of multi-biometrics for identification in different ways and Showed different results. This paper presents novel study on fusion strategies for personal identification using fingerprint and iris biometrics. The purpose of our paper is to investigate whether the integration of iris and fingerprint biometrics can achieve performance that may not be possible using a single biometric technology. We propose to use two activation function wavelet neural network for feature extraction and identification process after segments the fingerprint image into 16 blocks with (128*128) dimensions and segments the iris image into 32 blocks with (128*128) dimensions. The proposed method in this paper involves three steps. First reduced image size using wavelet packet 1-level decomposition , second feature extraction using two activation function wavelet neural network and identification using trained data and correlation for fingerprint and iris separately and finally fusion fingerprint and iris match scores to get the finally score for each person.