Biblio

When a complex scene such as rotation within a plane is encountered, the recognition rate of facial expressions will decrease much. A facial expression recognition algorithm based on CNN and LBP feature fusion is proposed in this paper. Firstly, according to the problem of the lack of feature expression ability of CNN in the process of expression recognition, a CNN model was designed. The model is composed of structural units that have two successive convolutional layers followed by a pool layer, which can improve the expressive ability of CNN. Then, the designed CNN model was used to extract the facial expression features, and local binary pattern (LBP) features with rotation invariance were fused. To a certain extent, it makes up for the lack of CNN sensitivity to in-plane rotation changes. The experimental results show that the proposed method improves the expression recognition rate under the condition of plane rotation to a certain extent and has better robustness.