Abstract
Recently, the Scale Invariant Feature Transform (SIFT) proposed by Lowe has emerged as a cut edge methodology in general object recognition as well as for other machine vision applications. However, SIFT method has not shown successful results in face recognition problem because of its original matching strategy which does not consider the location of local keypoints. This paper proposes a novel keypoints matching strategy for face recognition. The proposed matching strategy can avoid mis-matching of local keypoints by using regular grid of face image and can give robustness to various transformations by using keypoint voting strategy. By performing computational experiment on the AR face data set, we confirmed the proposed matching strategy gives better performance than the conventional methods. Especially, the proposed method can give robust and best performance for facial images with occlusions.
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Cho, M., Park, H. (2009). A Robust Keypoints Matching Strategy for SIFT: An Application to Face Recognition. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5863. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10677-4_82
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DOI: https://doi.org/10.1007/978-3-642-10677-4_82
Publisher Name: Springer, Berlin, Heidelberg
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