Latent Fingerprint Segmentation Based on Sparse Representation

Abstract

Latent fingerprints are the finger skin impressions which are left at the scene of a crime by accident. They are usually of poor quality with weak fingerprint ridge flows and various overlapping irrelevant patterns. It is still a challenging problem for automatic latent fingerprint processing and recognition. Latent fingerprint segmentation, which segments the fingerprint ridge area from complex backgrounds, is an important preprocessing step for latent fingerprint recognition. This paper proposes a latent fingerprint segmentation algorithm based on sparse representation. First, the total variation (TV) model is used to decompose a latent image into two components: texture and cartoon. The texture component, which contains the weak fingerprint ridge and valley structures, is used for further processing, while the cartoon component mainly consisting of the irrelevant information is discarded as noises. Then, we compute the sparse representation of the texture image against the dictionary constructed by a set of Gabor elementary functions. Since the sparse coefficients measure the weights of the basis atoms in fingerprint representation, an image quality measure is computed from the sparse coefficients, which evaluate how well the texture image can be sparsely reconstructed from the basis atoms. Finally, this image quality measure is used for fingerprint segmentation. We test the proposed method on the NIST SD27 latent fingerprint database. Experimental results and comparisons demonstrate the effectiveness of the proposed method.