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Proceedings Paper

Region-of-interest detection for fingerprint classification
Author(s): John M. Trenkle
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Paper Abstract

This paper discusses the use of neural networks to locate regions of interest for fingerprint classification using feature-encoded fingerprint images. The target areas are those useful for the classification of fingerprints: whorls, loops, arches, and deltas. Our approach is to limit the amount of data which a classification algorithm must consider by determining with high accuracy those areas which are most likely to contain relevant features (effective for classification). Several feature sets were analyzed and successful preliminary results are summarized. Five feature sets were tested: (1) grayscale data, (2) binary ridges, (3) binary projection, and (4 & 5) 4- and 8-way directional convolutions. Four-way directional convolution produced accurate results with a minimal number of false alarms. All work was conducted using fingerprint data from NIST Special Database 4. The approach discussed here is also applicable to other general computer vision problems. In addition to fingerprint classification, an example of face recognition is also provided to illustrate the generality of the algorithmic approach.

Paper Details

Date Published: 25 February 1994
PDF: 12 pages
Proc. SPIE 2103, 22nd AIPR Workshop: Interdisciplinary Computer Vision: Applications and Changing Needs, (25 February 1994); doi: 10.1117/12.169478
Show Author Affiliations
John M. Trenkle, Environmental Research Institute of Michigan (United States)

Published in SPIE Proceedings Vol. 2103:
22nd AIPR Workshop: Interdisciplinary Computer Vision: Applications and Changing Needs
J. Michael Selander, Editor(s)

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