Robustness of face recognition systems are measured by its ability to overcome the problem of changing in facial expression and rotation of individuals’ face images. This paper represents a face recognition system that overcomes the problem of changes in facial expressions in three-dimensional (3D) range images. We propose a novel geometric framework for analyzing 3D faces, with the specific goals of comparing, matching, and averaging their shapes. Here we represent facial surfaces by radial curves emanating from the nose tips and use elastic shape analysis of these curves to develop a Riemannian framework for analyzing shapes of full facial surfaces. A novel perception inspired non-metric partial similarity measure is introduced, which is potentially useful in deal with the concerned problems because it can help capturing the prominent partial similarities that are dominant in human perception. The effectiveness of the proposed method in handling large expressions, partial occlusions and other distortions is demonstrated on several well-known face databases.

SOM, 3D face

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