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The dominant local feature extractors are the Scale Invariant Feature Transform (SIFT) [7], and the Speeded-Up Robust Feature (SURF) [8], Due to their robustness and their time efficiency, SIFT and SURF have been deployed in a broad scope of applications such as object recognition [9], texture recognition, and image retrieval [10], [11], Thus, the two feature detectors have been selected for this research, and they have been applied on a standard fingerprint images database.
The contribution includes the development of SIFT and SURF local feature detectors on both CPU and GPU for processing a whole fingerprint database.
Section 3 sheds lights on the implementation methodologies, and explains the exhaustive evaluations of both SIFT and SURF feature detectors deployed on the both CPU and GPU, in terms of processing time, number of features, and features matching.