Wavelet-based Feature Extraction for Fingerprint Image Retrieval

This paper presents a novel approach to fingerprint retrieval for personal identification by joining three image retrieval tasks, namely, feature extraction, similarity measurement, and feature indexing, into a wavelet-based fingerprint retrieval system. We propose the use of different types of Wavelets for representing and describing the textural information present in fingerprint images. For that purposes, the feature vectors used to characterize the fingerprints are obtained by computing the mean and the standard deviation of the decomposed images in the Wavelet domain. These feature vectors are used to retrieve the most similar fingerprints given a query image, while their indexation is used to reduce the search spaces of image candidates. The different types of Wavelets used in our study include: Gabor Wavelets (GWs), Tree-Structured Wavelet Decomposition using both Orthogonal Filter Banks (TOWT) and Bi-orthogonal Filter Banks (TBOWT), as well as the Steerable Wavelets. To evaluate the retrieval accuracy of the proposed approach, a total number of eight different data sets were used. Experiments also evaluated different combinations of Wavelets with six similarity measures. The results show that the Gabor Wavelets combined with the Square Chord similarity measure achieves the best retrieval effectiveness.