Analysis, Comparison, and Assessment of Latent Fingerprint Image Preprocessing

Haiying Guan, Paul Lee, Andrew Dienstfrey, Mary Theofanos, Curtis Lamp, Brian Stanton, Matthew T. Schwarz; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 126-133


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 implement a study in which a group of trained Latent Print Examiners provide Extended Feature Set markups of all 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.

Related Material

author = {Guan, Haiying and Lee, Paul and Dienstfrey, Andrew and Theofanos, Mary and Lamp, Curtis and Stanton, Brian and Schwarz, Matthew T.},
title = {Analysis, Comparison, and Assessment of Latent Fingerprint Image Preprocessing},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {July},
year = {2017}