Latent fingerprint enhancement based on orientation guided sparse representation

Abstract

Latent fingerprints are the finger skin impressions left at the crime scene by accident. They are usually of poor quality with unclear ridge structure and various overlapping patterns. This paper proposes a latent fingerprint enhancement algorithm which combines the TV image decomposition model and image reconstruction by orientation guided sparse representation. Firstly, the TV model is applied to decompose a latent fingerprint image into the texture and cartoon components. Secondly, we calculate the orientation field and the reliability of the texture image. Finally, for the low reliability region, sparse representation based on the redundant dictionary, which is constructed with Gabor functions and the specific local ridge orientation, is iteratively used to reconstruct the image. Experimental results based on NIST SD27 latent fingerprint database indicate that the proposed algorithm can not only remove various noises, but also restore the corrupted ridge structure well.