Abstract
Based on the observation that facial surfaces across different expressions can be modeled as similar isometric transformations, in this paper a novel deformation invariant image for robust 3D face recognition is proposed. First, we obtain the depth image and the intensity image from the original 3D facial data. Then, geodesic level curves are generated by constructing radial geodesic distance image from the depth image. Finally, deformation invariant image is constructed by evenly sampling points from the selected geodesic level curves in the intensity image. Our experiments are based on the 3D CASIA Face Database, which includes 123 individuals with complex expressions. Experimental results show that our proposed method substantially improves the recognition performance under various facial expressions.
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