Analysis, Comparison, and Assessment of Latent Fingerprint Preprocessing

Published

July 20, 2017

Author(s)

Haiying Guan, Paul Y. Lee, Curtis L. Lamp, Andrew Dienstfrey, Mary Frances Theofanos, Brian Stanton, Matthew Schwarz

Abstract

Latent fingerprints obtained from crime scenes are rarely immediately suitable for identification purposes. Instead, most latent fingerprint images must be preprocessed to enhance the fingerprint information held within the digital image, while suppressing interference arising from noise and otherwise unwanted image features. In the following we present results of our ongoing research to assess this critical step in the forensic workflow. Previously we discussed the creation of a new database of latent fingerprint images to support such research. The new contributions of this paper are twofold. First, we augment this database by contracting a large group of trained Latent Print Examiners to provide Extended Features Set markups of the latent images. We discuss the experimental design of this study, and its execution. Next we propose metrics for measuring the increase of fingerprint information provided by latent fingerprint image preprocessing, and we present preliminary analysis of these metrics when applied to the images in our database. We consider formally defined quality scales (Good, Bad, Ugly), and minutiae identifications of latent fingerprint images before and after preprocessing. All analyses show that latent fingerprint image preprocessing results in a statistically significant increase in fingerprint information and quality.

Proceedings Title

CVPR 2017

Conference Dates

July 22-25, 2017

Conference Location

Honolulu, HI, US

Pub Type

Conferences

Download Paper

Local Download

Keywords

latent fingerprint pre-processing, forensic, biometrics

Citation

Guan, H. , Lee, P. , Lamp, C. , Dienstfrey, A. , Theofanos, M. , Stanton, B. and Schwarz, M. (2017), Analysis, Comparison, and Assessment of Latent Fingerprint Preprocessing, CVPR 2017, Honolulu, HI, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=923087 (Accessed November 23, 2024)