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Facial expressions recognition (FER) of 3D face scans has received asignificant amount of attention in recent years. Most of the facial expressionrecognition methods have been proposed using mainly 2D images. These methodssuffer from several issues like illumination changes and pose variations.Moreover, 2D mapping from 3D images may lack some geometric and topologicalcharacteristics of the face. Hence, to overcome this problem, a multi-modal 2D+ 3D feature-based method is proposed. We extract shallow features from the 3Dimages, and deep features using Convolutional Neural Networks (CNN) from thetransformed 2D images. Combining these features into a compact representationuses covariance matrices as descriptors for both features instead ofsingle-handedly descriptors. A covariance matrix learning is used as a manifoldlayer to reduce the deep covariance matrices size and enhance theirdiscrimination power while preserving their manifold structure. We then use theBag-of-Features (BoF) paradigm to quantize the covariance matrices afterflattening. Accordingly, we obtained two codebooks using shallow and deepfeatures. The global codebook is then used to feed an SVM classifier. Highclassification performances have been achieved on the BU-3DFE and Bosphorusdatasets compared to the state-of-the-art methods.

Deep and Shallow Covariance Feature Quantization for 3D Facial Expression Recognition