Choosing best basis in wavelet packets for fingerprint matching

Fingerprint matching has been deployed in a variety of security related applications. Traditional minutiae detection based identification algorithms do not utilize the rich discriminatory texture structure of fingerprint images. Furthermore, minutiae detection requires substantial improvement of image quality and is thus error-prone. In this paper, we propose a new algorithm for fingerprint identification using wavelet packet analysis and best basis selection. Each fingerprint is decomposed using two dimensional wavelet packet family corresponding to different scales. The energy distribution of the fingerprint in each subband is extracted as a feature for identification. Wavelet packet decomposition yields a redundant representation of the image. For this reason, several algorithms for selecting the best basis from this redundant representation have been investigated. In this paper, we propose a new method for choosing best basis in wavelet packets for fingerprint matching. Experiments show that our new algorithm improves the accuracy of fingerprint matching.