Learning Facial Expression Codes with Sparse Auto-Encoder

Abstract

A sparse auto-encoder model was trained to extract the code of different facial expression, which comprises four encoder layers and three decode layers, the representation locating in the fourth layer (code layer) is the features expected. With large amounts of patches randomly selected from training faces, the model was trained firstly via backpropagation which minimizes an unsupervised sparse reconstruction error, and then a softmax classifier was learned for supervised classification. The input vector for the classification is the feature of facial image induced by the learned sparse auto-encoder and two key operations (convolving and pooling). Using a small number of hidden units per layer and a relatively small number of training set, the proposed model achieves excellent performance in the experiments.