Type of Document Dissertation Author Short, Nathaniel Jackson URN etd-09192012-221225 Title Robust Feature Extraction and Temporal Analysis for Partial Fingerprint Identification Degree PhD Department Electrical and Computer Engineering Advisory Committee
Advisor Name Title Abbott, A. Lynn Committee Chair Fox, Edward Alan Committee Member Hsiao, Michael S. Committee Member Shukla, Sandeep K. Committee Member Xuan, Jianhua Jason Committee Member Keywords
- Bayesian Estimation
- Extended Features
- Temporal Analysis
Date of Defense 2012-09-05 Availability unrestricted AbstractIdentification of an individual from discriminating features of the friction ridge surface is one of the oldest and most commonly used biometric techniques. Methods for identification span from tedious, although highly accurate, manual examination to much faster Automated Fingerprint Identification Systems (AFIS). While automatic fingerprint recognition has grown in popularity due to the speed and accuracy of matching minutia features of good quality plain-to-rolled prints, the performance is less than impressive when matching partial fingerprints. For some applications, including forensic analysis where partial prints come in the form of latent prints, it is not always possible to obtain high-quality image samples. Latent prints, which are lifted from a surface, are typically of low quality and low fingerprint surface area. As a result, the overlapping region in which to find corresponding features in the genuine matching ten-print is reduced; this in turn reduces the identification performance. Image quality also can vary substantially during image capture in applications with a high throughput of subjects having limited training, such as in border control. The rushed image capture leads to an overall acceptable sample being obtained where local image region quality may be low.
We propose an improvement to the reliability of features detected in exemplar prints in order to reduce the likelihood of an unreliable overlapping region corresponding with a genuine partial print. A novel approach is proposed for detecting minutiae in low quality image regions. The approach has demonstrated an increase in match performance for a set of fingerprints from a well-known database. While the method is effective at improving match performance for all of the fingerprint images in the database, a more significant improvement is observed for a subset of low quality images.
In addition, a novel method for fingerprint analysis using a sequence of fingerprint images is proposed. The approach uses the sequence of images to extract and track minutiae for temporal analysis during a single impression, reducing the variation in image quality during image capture. Instead of choosing a single acceptable image from the sequence based on a global measure, we examine the change in quality on a local level and stitch blocks from multiple images based on the optimal local quality measures.
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