level 2 features and wavelet analysis based hybrid fingerprint matcher
In this work, we present a hybrid fingerprint verification system based on Level 2 features i.e. minutiae and multiresolution analysis of fingerprint images. Systems based only on minutiae features do not perform well for poor quality images. In practice, we often encounter extremely dry, wet fingerprint images with cuts, warts, etc. Due to such fingerprints, minutiae based systems show poor performance for real time authentication applications with large number of identities. To alleviate the problem of poor quality fingerprints, and to improve overall performance of the system, we have proposed hybrid fingerprint verification based on both minutiae features and wavelet statistical features. Final matching score is calculated by fusing two matching scores of minutiae based method and wavelet based algorithm. Proposed system is tested on DB1 (set A) database of FVC 2004. The experimental results have shown that, proposed approach is more efficient and suitable than conventional minutiae based methods for real time authentication systems with large size databases.
wavelet fingerprint minutiae authentication biometrics