Effect of resolution and image quality on combined optical and neural network fingerprint matching

This paper presents results on direct optical matching, using Fourier trans forms and neural networks for matching fingerprints for authentication. Dir ect optical correlations and hybrid optical neural network correlation are used in the matching system. The test samples used in the experiments are t he fingerprints taken from NIST database SD-9. These images, in both binary and gray-level forms, are stored in a VanderLugt correlator (A. VanderLugt , Signal detection by complex spatial filtering, IEEE Trans. Inform. Theory IT-10 (1964) 139-145). Tests of typical cross correlations and autocorrela tion sensitivity for both binary and 8 bit gray images are presented. When Fourier transform (FT) 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, 90.9 % matching accuracy is obtained on a test set of 200,000 image pairs. These results are obtained on images using 512 pixel resolution. The effect of i mage quality and resolution are tested using 256 and 128 pixel images, and yield accuracy of 89.3 and 88.7%. The 128-pixel images show only ridge flow and have no reliably detectable ridge endings or bifurcations and are ther efore not suitable for minutia matching. This demonstrates that Fourier tra nsform matching and neural networks can be used to match fingerprints which have too low image quality to be matched using minutia-based methods. Sinc e more than 258,000 images were used to test each hybrid system, this is th e largest test to date of FT matching for fingerprints. Published by Elsevi er Science Ltd.