Robust 3D Face Recognition by Using Shape Filtering

Achieving high accuracy in the presence of expression variation remains one of the most challenging aspects of 3D face recognition. In this paper, we propose a novel recognition approach for robust and efficient matching. The framework is based on shape processing filters that divide face into three components according to its frequency spectral. Low-frequency band mainly corresponds to expression changes. High-frequency band represents noise. Mid-frequency band is selected for expression-invariant feature which contains most of the discriminative personal-specific deformation information. By using shape filter, it offers a dramatic performance improvement for both accuracy and robustness. We conduct extensive experiments on FRGC v2 databases to verify the efficacy of the proposed algorithm, and validate the above claims.