关键词: 人脸表情识别（FER）, 深度学习, 尺度不变特征变换（SIFT）, 模型融合, 小样本

Abstract:

In order to further improve the accuracy of facial expression recognition in small samples, a small sample expression recognition method based on deep learning and fusion of different models is proposed. In this method, a single CNN (convolutional neural network) model is compared, and the relatively appropriate CNN is selected by preserving probability [p] of different nodes in the dropout layer. Then, the scale-invariant feature transformation (SIFT) algorithm is used to extract features. The purpose of extracting features with SIFT is to improve the performance of small data. And then, in order to reduce the error, and avoid over fitting, all the models are carried on summary，and the model CNN-SIFT-AVG (convolutional neural network and scale-invariant feature trans-formation average) is obtained by simple average model fusion method. Finally, only a few sample data are used to train the model. The model is tested on FER2013, CK+ and JAFFE datasets. Experimental results show that this model can greatly improve the accuracy of small sample facial expression recognition, and produce excellent results in FER2013, CK+ and JAFFE datasets, with a maximum improvement of about 6% compared with other facial expression recognition methods.

Key words: facial expression recognition (FER), deep learning, scale-invariant feature transformation (SIFT), model fusion, small sample