Latent fingerprint segmentation based on linear density

Abstract

Latent fingerprints are the finger skin impressions left at the criminal scene unintentionally, which are important evidence for law enforcement agencies to identify criminals. Most of latent fingerprint images are of poor quality with unclear ridge structure and various non-fingerprint patterns. Segmentation is an important processing step to separate the fingerprint foreground from the background for more accurate and efficient feature extraction and identification. Traditional fingerprint segmentation methods are based on the information of gradients and local properties, which is sensitive to noise. This paper proposes a latent fingerprint segmentation algorithm based on linear density. First, a total variation (TV) image model is used to decompose a latent image into the cartoon and texture components. The texture component consisting of the latent fingerprint is used for further processing while the cartoon component is removed as noise. Second, we propose to detect a set of line segments from the texture image and compute the linear density map which can characterize the interleaved ridge and valley structure well. Finally, a segmentation mask is generated by thresholding the linear density map. The proposed method is tested on NIST SD27 latent fingerprint database. Experimental results and comparisons demonstrate the effectiveness of the proposed method.