  In this paper, covariance matrices are exploited to encode the deep
convolutional neural networks (DCNN) features for facial expression
recognition. The space geometry of the covariance matrices is that of Symmetric
Positive Definite (SPD) matrices. By performing the classification of the
facial expressions using Gaussian kernel on SPD manifold, we show that the
covariance descriptors computed on DCNN features are more efficient than the
standard classification with fully connected layers and softmax. By
implementing our approach using the VGG-face and ExpNet architectures with
extensive experiments on the Oulu-CASIA and SFEW datasets, we show that the
proposed approach achieves performance at the state of the art for facial
expression recognition.
