EI3D: Expression-invariant 3D face recognition based on feature and shape matching

This paper presents a local feature based shape matching algorithm for expression-invariant 3D face recognition. Each 3D face is first automatically detected from a raw 3D data and normalized to achieve pose invariance. The 3D face is then represented by a set of keypoints and their associated local feature descriptors to achieve robustness to expression variations. During face recognition, a probe face is compared against each gallery face using both local feature matching and 3D point cloud registration. The number of feature matches, the average distance of matched features, and the number of closest point pairs after registration are used to measure the similarity between two 3D faces. These similarity metrics are then fused to obtain the final results. The proposed algorithm has been tested on the FRGC v2 benchmark and a high recognition performance has been achieved. It obtained the state-of-the-art results by achieving an overall rank-1 identification rate of 97.0% and an average verification rate of 99.01% at 0.001 false acceptance rate for all faces with neutral and non-neutral expressions. Further, the robustness of our algorithm under different occlusions has been demonstrated on the Bosphorus dataset.

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