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A Scale Invariant Feature Transform Based Method

In this paper, we propose a novel method for face recognition. Its basic idea is to use a coarse to fine strategy based on Scale Invariant Feature Transform (SIFT) feature. To recognize a test sample, our method contains the three main steps: The first step identifies a certain number of candidates from training samples, depending on the Euclidean distance between the test sample and all training samples. The second step counts the numbers of well matched pairs of SIFT features between each candidate chosen in the first step and the test sample, then chooses several samples from candidates with the greater number of well matched pairs. The third step calculates the similarity between the test sample and each class that includes training samples chosen in second step, and chooses a class with the highest similarity to be the recognition result. By the first two steps, our method succeeds in greatly reducing the computational complexity, and avoiding the interference of those samples that may cause error recognition to a certain extent. The third step enhances the robustness of our method. Extensive experiments on different public face databases confirm that our method obtains high recognition accuracy and has a good robustness.