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• W1970082170 abstract "To recognize faces with different facial expressions or varying views from only one stored prototype per person is challenging. This paper presents such a system based on both 3D range data as well as the corresponding 2D gray-level facial images. The traditional 3D Gabor filter (3D TGF) is explored in the face recognition domain to extract expression-invariant features. To extract view-invariant features, a rotation-invariant 3D spherical Gabor filter (3D SGF) is proposed. Furthermore, a two-dimensional (2D) Gabor histogram is employed to represent the Gabor responses of the 3D SGF for solving the missing-point problem caused by self-occlusions under large rotation angles. The choice of 3D Gabor filter parameters for face recognition is examined as well. To match a given test face with each model face, the Least Trimmed Square Hausdorff Distance (LTS-HD) is employed to tackle the possible partial-matching problem. Experimental results based on our face database involving 80 persons have demonstrated that our approach outperforms the standard Eigenface approach and the approach using the 2D Gabor-wavelets representation." @default.
• W1970082170 created "2016-06-24" @default.
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• W1970082170 date "2005-10-01" @default.
• W1970082170 modified "2023-10-01" @default.
• W1970082170 title "Face recognition from 2D and 3D images using 3D Gabor filters" @default.
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• W1970082170 doi "https://doi.org/10.1016/j.imavis.2005.07.005" @default.
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