Latent Fingerprints Segmentation: Feasibility of Using Clustering-Based Automated Approach

Abstract

Latent fingerprints are the impressions of ridges left on a crime scene due to unintentional touching of criminal’s fingers on different objects. These impressions of ridges have been used as vital evidences for identifying the criminals by law enforcement agencies. In this study, an attempt for automated segmentation of latent fingerprint has been made using K-means clustering. In proposed approach, Sobel filter and morphological operations have been used for background evaluation and image enhancement. Clustering is used to classify image data into k clusters to separate the foreground and background information. Mask has been generated on the basis of clustered data and is used to obtain the segmentation of latent fingerprints. Simulation results of the proposed algorithm show significant improvement in terms of missed detection rate (MDR) and false detection rate (FDR) using NIST SD-27 latent fingerprint database. Segmentation results reveal the MDR of 1.80, 4.75 and 7.80% and FDR of 17.85, 26.28 and 34.05% for good, bad and ugly quality of latent fingerprints, respectively. Moreover, visual segmentation reliability (VSR) is upto 90% for good quality images and varies in the range of 70–80% for bad quality latent fingerprints, whereas VSR for ugly fingerprints is in the range of 50–60%.