Latent fingerprint detection and segmentation with a directional total variation model

Abstract

Latent fingerprint detection and segmentation play a critical role in image forensics for law enforcement. Being collected from crime scenes, a latent fingerprint is often mixed with other components such as structured noise or other fingerprints. Existing fingerprint recognition algorithms fail to work properly for latent fingerprint images, since they are mostly applicable under the assumption that the image is already properly segmented and there is no overlap between the target fingerprint and other components. In this work, we present a novel directional total variation (DTV) model to achieve effective latent fingerprint detection and segmentation. As compared with existing total variation models, the proposed DTV model differentiates itself by considering spatial-dependent texture orientations in the TV computation, which is particularly suitable for images with oriented textures. We demonstrate the superior performance of the proposed DTV technique using images from the NIST SD27 latent fingerprint database.