Latent Fingerprint Segmentation Based on Ridge Density and Orientation Consistency

Abstract

Latent fingerprints are captured from the fingerprint impressions left unintentionally at the surfaces of the crime scene. They are often used as an important evidence to identify criminals in law enforcement agencies. Different from the widely used plain and rolled fingerprints, the latent fingerprints are usually of poor quality consisting of complex background with a lot of nonfingerprint patterns and various noises. Latent fingerprint segmentation is an important image processing step to separate fingerprint foreground from background for more accurate and efficient feature extraction and matching. Traditional methods are usually based on the local features such as gray scale variance and gradients, which are sensitive to noise and cannot work well for latent images. This paper proposes a latent fingerprint segmentation method based on combination of ridge density and orientation consistency, which are global and local features of fingerprints, respectively. First, a texture image is obtained by decomposition of latent image with a total variation model. Second, we propose to detect the ridge segments from the texture image, and then compute the density of ridge segments and ridge orientation consistency to characterize the global and local fingerprint patterns. Finally, fingerprint segmentation is performed by combining the ridge density and orientation consistency for latent images. The proposed method has been evaluated on NIST SD27 latent fingerprint database. Experimental results and comparison demonstrate the promising performance of the proposed method.

Interpol review of fingermarks and other body impressions 2016–2019

“… Improvement on the computation of orientation fields of fingerprint patterns including marks on complex backgrounds [ [154] , [155] , [156] ] and palmar impressions [ 157 ]. Automatic segmentation of fingermark images against complex backgrounds [ [158] , [159] , [160] , [161] ], including overlapping marks [ 162 ] or using deep learning techniques such as convolutional neural networks [ 163 ]. For a review of segmentation methods, refer to Ref.…”

“…Automatic segmentation of fingermark images against complex backgrounds [ [158] , [159] , [160] , [161] ], including overlapping marks [ 162 ] or using deep learning techniques such as convolutional neural networks [ 163 ]. For a review of segmentation methods, refer to Ref.…”

Implementation of a Transform-Minutiae Fusion-Based Model for Fingerprint Recognition

“…Broadly features of a fingerprint image can be grouped into two: global and local. e global fingerprint features include delta and core points also known as singular points, as well as the ridge orientation and spacing [14].…”

“…e objects of interest in the foreground and background of a given image correspond to the C 1 and C 2 , respectively. Equations (13) and (14) are the respective probabilities:…”