Achieving high fidelity 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 spectra. Low-frequency band mainly corresponds to expression changes. High-frequency band represents noise. Mid-frequency band is selected for expression-invariant feature that contains most of the discriminative personal-specific deformation information. After bijectively mapping facial mesh into square domain based on mesh parametrization

we obtain 2D geometry image of 3D shape with linear interpolation for face matching. We conduct extensive experiments on FRGC v2 databases to verify the efficacy of the proposed algorithm

and validate that by using shape filter

it offers a performance improvement for both accuracy and robustness.