Latent Fingerprint Segmentation Based on Ridge Density and Orientation Consistency
Joint Authors
Liu, Manhua
Liu, Shuxin
Yan, Weiwu
Source
Security and Communication Networks
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-05-17
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Liu, Manhua& Liu, Shuxin& Yan, Weiwu. 2018. Latent Fingerprint Segmentation Based on Ridge Density and Orientation Consistency. Security and Communication Networks،Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1214149
Modern Language Association (MLA)
Liu, Manhua…[et al.]. Latent Fingerprint Segmentation Based on Ridge Density and Orientation Consistency. Security and Communication Networks No. 2018 (2018), pp.1-10.
https://search.emarefa.net/detail/BIM-1214149
American Medical Association (AMA)
Liu, Manhua& Liu, Shuxin& Yan, Weiwu. Latent Fingerprint Segmentation Based on Ridge Density and Orientation Consistency. Security and Communication Networks. 2018. Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1214149
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1214149