This paper proposes a novel fingerprint enhancement algorithm based on contextual filtering in DCT domain. All intrinsic fingerprint features including ridge orientation and frequency are estimated simultaneously from DCT analysis, resulting in fast and efficient implementation. In addition, the proposed approach takes advantage of frequency-domain enhancement resulting in best performance in high curvature area. Comparing with DFT domain, DCT has better signal energy compaction and perform faster transform with real coefficients. Moreover, the experimental results show that the DCT approach is out-performed the traditional Gabor filtering, including the fastest separable Gabor filter, in both quality and computational complexity.

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Abstract. Performance and computational complexity comparisons of various block-based fingerprint enhancement schemes are tested and reported in this literature. Enhancement performance is evaluated by comparing equal error rates, which obtained by a proposed fingerprint matching algorithm using local and global features. Various enhancement methods are tested; i.e. three types of spatial Gabor filtering, short-time Fourier transform filtering, and discrete cosine transform filtering. These enhancement schemes also tested with various databases such as FVC2000, FVC2002, and FVC2004. Finally, computational complexity of enhancement implementation is analyzed and concluded.

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