Fingerprint image enhancement using a binary angular representation

We explore a novel approach to enhancing fingerprint images using a new binary directional filter bank (DFB). Automated fingerprint identification systems (AFIS) are used to classify a fingerprint in a large volume of images. Many approaches to AFIS have been suggested, most sharing in common the idea of extracting discriminate feature representations. As part of that process, the raw fingerprints are often smoothed, converted to binary and thinned. Conventional directional methods, which have been used successfully in the past, provide representations that delineate the directional components in the fingerprint image enabling separation, and enhancement. Our binary DFB receives a binary input and outputs a binary image set comprised of directional components. Through proper weighting and manipulation of the subbands, specific features within the fingerprint can be enhanced. We propose a new enhancement approach that remains in the binary domain for the entire process. This paper provides a description of a new binary DFB and its application to fingerprint pre-processing