Memory-efficient Fingerprint Verification

Fingerprint recognition and verification are often based on local fingerprint features, usually ridge endings or terminations, also called minutiae. By exploiting the structural uniqueness of the image region around a minutia, the fingerprint recognition performance can be significantly enhanced. However, for most fingerprint images the number of minutia image regions (MIR’s) becomes dramatically large, which imposes especially for embedded systems an enormous memory requirement. Therefore, we are investigating different algorithms for compression of minutia regions. The requirement for these algorithms is to achieve a high compression rate (about 20) with minimum loss in the matching performance of minutia image region matching. In this paper we investigate the matching performance for compression algorithms based on the Principal Component and the wavelet transformation. The matching results are presented in form of normalized ROC curves and interpreted in terms of compression rates and the MIR dimension.