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Publication Citation: A New Metric for Latent Fingerprint Image Preprocessing

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Author(s): Mary F. Theofanos; Andrew M. Dienstfrey; Brian C. Stanton; Haiying Guan;
Title: A New Metric for Latent Fingerprint Image Preprocessing
Published: June 24, 2013
Abstract: Although fingerprint recognition is a well-studied area, forensic fingerprint preprocessing based on computational theory is still a relatively new domain in need of further scientific study and development of standards of best practice. Latent fingerprint preprocessing is a common step in the forensic analysis workflow that is performed to improve image quality for subsequent identification analysis. Due to the low quality of the latent fingerprint images, the fingerprint preprocessing is especially crucial to the success of the final fingerprint identification in the forensic fingerprint image examination. In this paper we isolate forensic fingerprint image preprocessing for deeper analysis. We first provide a brief review of latent fingerprint preprocessing. Next we present our work to extend Spectral Image Validation and Verification (SIVV) to serve as a metric for latent fingerprint image quality, SIVV analysis 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 the fingerprint preprocessing.
Proceedings: CVPR 2013 Workshops on Biometrics
Pages: pp. 84 - 91
Location: Portland, OR
Dates: June 23-28, 2013
Keywords: Forensic Latent Fingerprint, Fingerprint Preprocessing, Fingerprint Quality, Metric
Research Areas: Biometrics
PDF version: PDF Document Click here to retrieve PDF version of paper (1MB)