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This paper proposes a novel 4D Facial Expression Recognition (FER) methodusing Collaborative Cross-domain Dynamic Image Network (CCDN). Given a 4D dataof face scans, we first compute its geometrical images, and then combine theircorrelated information in the proposed cross-domain image representations. Theacquired set is then used to generate cross-domain dynamic images (CDI) viarank pooling that encapsulates facial deformations over time in terms of asingle image. For the training phase, these CDIs are fed into an end-to-enddeep learning model, and the resultant predictions collaborate over multi-viewsfor performance gain in expression classification. Furthermore, we propose a 4Daugmentation scheme that not only expands the training data scale but alsointroduces significant facial muscle movement patterns to improve the FERperformance. Results from extensive experiments on the commonly used BU-4DFEdataset under widely adopted settings show that our proposed method outperformsthe state-of-the-art 4D FER methods by achieving an accuracy of 96.5%indicating its effectiveness.

Automatic 4D Facial Expression Recognition via Collaborative Cross-domain Dynamic Image Network