Dept of CS and IT

Personal identification problem has been a major field of research in recent years, Biometrics-based technologies that exploit fingerprints, iris, face, voice and palmprints, have been in the center of attention to solve this problem .Palmprint based biometric method has gained high impact over the other biometric methods due to its ease of acquisition, reliability and high client acceptance. This paper present fusion of Multispectral palmprint images using wavelet decomposition for personnel identity verification, for that purpose Polytechnic Hong Kong University database is used. The objective of image fusion is to combine information from multiple images of the same scene. The result of image fusion is a new image which is more informative. And complete than any of the input image. Wavelet-based image fusion method is used as fusion strategy in our schema we have done fusion of Approximation Coefficient of RGB, NIR images, got the fused image by applying the DWT technique, to decided the quality of image, used the image quality assessment like MSE, PSNR, Higher PSNR value provide higher image quality while Higher MSE value provide lower image quality. By measuring the quality of images (Fused img+ RGB, NIR img), it is found that the quality of NIR is higher 7.4439 in nature while Green is low 1.6204 in quality as compare to the other spectral band..

Palmprint is important member of biometric family, different types of algorithms and system have been proposed and great success has been achieved in Palmprint research, most of the previous Palmprint recognition works use white light source of illumination, which does not highlights the more feature these problem is solved by spectral band of multispectral Palmprint. This paper present feature level image fusion of multispectral Palmprint images for that purpose Polytechnique Hongkong University database is used. Initially the images were subjected to some preprocessing operation like filtering. Wavelet theory is introduced to resolve the Palmprint features extraction problem, and for matching purpose distance matrix is calculated by using Euclidian distance. Wavelet-based image fusion method is used as fusion strategy in our schema we have done fusion of Approximation Coefficient of RGB, NIR images, got the fused image by applying the DWT technique, to reduce the high dimensionality 2 nd order derivatives is convolved with fused image. The threshold value range decided with maximum and minimum distance of two images, and we found that, as we increased the threshold FAR will be increased and FRR will be decreased, as well as the recognition rate will be increased. We have to select recognition rate where false acceptance and rejection are low. At a given threshold a biometric system that gives low FAR and low FRR is good one. So in our experiments at the threshold value 0.33 gives the 90% recognition rate.