关键词: 人脸表情识别, 堆栈式混合自编码器（SHAE）, 稀疏自编码器（SAE）, 去噪自编码器（DAE）

Abstract: To further improve the recognition rate of facial expressions, a face recognition method based on deep learning and Stacked Hybrid Auto-Encoder（SHAE） is adopted. The structure of the method is a 5-layer network structure composed of a Denoising Auto-Encoder（DAE）, a Sparse Auto-Encoder（SAE）, and an Auto-Encoder（AE）. In order to increase the robustness and generalization ability of the network, a DAE is used to extract features from the samples. In order to reduce the dimensions of the extracted features and to extract further abstract sparse features, a SAE is used for cascading, and further processing of features. The training process begins with pre-training and overall fine-tuning of the unlabeled data, initializing and updating the weight of the whole structure, and then testing and training with labeled data. Experiments on two datasets, JAFFE and CK+, show that this method has a better recognition effect than a purely stacked DAE or apurely stacked SAE.

Key words: face expression recognition, Stacked Hybrid Auto-Encoder, Sparse Auto-Encoder, Denoising Auto-Encoder