A New Metric for Latent Fingerprint Image Preprocessing

Haiying Guan, Andrew M. Dienstfrey, Mary Frances Theofanos; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2013, pp. 84-91

Abstract

We propose a new image-based metric and explore its utility as a quality diagnostic for fingerprint image preprocessing. Due to the low quality of the latent fingerprint images, preprocessing is a common step in the forensic analysis workflow, and furthermore is critical to the success of fingerprint identification. Whereas fingerprint analysis is a well-studied field with a deep history, forensic image preprocessing is a relatively new domain in need of research and development of analysis and best practice guidance. Our new metric is based on an extension of the Spectral Image Validation and Verification (SIVV) [1]. SIVV was originally developed to differentiate ten-print or rolled fingerprint images from other non-fingerprint images such as face or iris images. Several modifications are required to extend SIVV analysis to the latent space. We propose, implement, and test this new SIVV-based metric to measure latent fingerprint image quality and the effectiveness of the forensic latent fingerprint preprocessing step. Preliminary results show that this new metric can provide positive indications of both latent fingerprint image quality, and the effectiveness of fingerprint preprocessing. t

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[bibtex]
@InProceedings{Guan_2013_CVPR_Workshops,
author = {Guan, Haiying and Dienstfrey, Andrew M. and Theofanos, Mary Frances},
title = {A New Metric for Latent Fingerprint Image Preprocessing},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2013}
}