Fingerprint image enhancement using a binary angular representation

In this paper, we explore a novel approach to enhancing ngerprint images using a new binary directional lter bank (DFB). Automated ngerprint identi cation systems (AFIS) are used to classify a ngerprint 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 ngerprints 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 ngerprint 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, speci c features within the ngerprint 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 ngerprint pre-processing.