Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/12317
Title: Elastic shape-texture matching for human face recognition
Authors: Xie, X
Lam, KM 
Keywords: Elastic shape-texture matching
Face recognition
Gabor wavelets
Hausdorff distance
Issue Date: 2008
Publisher: Elsevier
Source: Pattern recognition, 2008, v. 41, no. 1, p. 396-405 How to cite?
Journal: Pattern recognition 
Abstract: In this paper, a novel, elastic, shape-texture matching method, namely ESTM, for human face recognition is proposed. In our approach, both the shape and the texture information are used to compare two faces without establishing any precise pixel-wise correspondence. The edge map is used to represent the shape of an image, while the texture information is characterized by both the Gabor representations and the gradient direction of each pixel. Combining these features, a shape-texture Hausdorff distance is devised to compute the similarity of two face images. The elastic matching is robust to small, local distortions of the feature points such as those caused by facial expression variations. In addition, the use of the edge map, Gabor representations and the direction of the image gradient can all alleviate the effect of illumination to a certain extent. With different databases, experimental results show that our algorithm can always achieve a better performance than other face recognition algorithms under different conditions, except when an image is under poor and uneven illumination. Experiments based on the Yale database, AR database, ORL database and YaleB database show that our proposed method can achieve recognition rates of 88.7%, 97.7%, 78.3% and 89.5%, respectively.
URI: http://hdl.handle.net/10397/12317
ISSN: 0031-3203
EISSN: 1873-5142
DOI: 10.1016/j.patcog.2007.06.008
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