Combined optical and neural network fingerprint matching

Charles L. Wilson; Craig I. Watson; Eung Gi Paek

doi:10.1117/12.270385

27 March 1997 Combined optical and neural network fingerprint matching

Charles L. Wilson, Craig I. Watson, Eung Gi Paek

Proceedings Volume 3073, Optical Pattern Recognition VIII; (1997) https://doi.org/10.1117/12.270385
Event: AeroSense '97, 1997, Orlando, FL, United States

Abstract

This paper presents results on direct optical matching of inked and real-time fingerprint images. Direct optical correlations and hybrid optical neural network correlation are used in the matching system for inked fingerprints. Preliminary results on optical matching of real-time fingerprints use optical correlation. The test samples used in the inked image experiments are the fingerprint taken from NIST database SD-9. These images, in both binary and gray level forms, are stored in a VanderLugt correlator. Tests of typical cross correlations and auto correlation sensitivity for both binary and 8 bit gray images are presented. When global correlations are tested on a second inked image results are found to be strongly influenced by plastic distortion of the finger. When the correlations are used to generate features that are localized to parts of each fingerprint and combined using a neural network classification network and separate class-by-class matching networks, 84.3 percent matching accuracy is obtained on a test set of 100,000 image pairs. Initial results with real- time images suggest that the difficulties resulting from finger deformation can be avoided by combining many different distorted images when the hologram is constructed in the correlator. Testing this process will require analysis of 10-20 second sequences of digital video.

Citation Download Citation

Charles L. Wilson, Craig I. Watson, and Eung Gi Paek "Combined optical and neural network fingerprint matching", Proc. SPIE 3073, Optical Pattern Recognition VIII, (27 March 1997); https://doi.org/10.1117/12.270385

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