关键词: 表情识别, Gabor核, 卷积神经网络, 自动编码器, 遗传算法, 支持向量机（SVM）, 参数优化

Abstract:

Facial expression recognition is one of the hotspots in the field of computer vision. Aiming at the problems that the extracted high-dimensional features recognition rate of facial expression images is poor and the generalization is almost unsatisfactory, combined with the advantages of convolutional neural network to learn local features, this paper proposes a method of multi-channel, image segmentation and optimization of Gabor convolution kernel parameters to realize image convolution, and analyzes the extracted expression adaptive features. Automatic encoder theory is used to realize high dimensional feature dimension reduction and multi-channel feature fusion. Because the traditional SVM（Support Vector Machine） is not sensitive to multi-classification tasks, the genetic algorithm is used to optimize the maximum interval classification surface and obtain fitter classifier parameters. Finally, the designed GaAeS-net（Gabor Autoencoder Support Vector Machine Convolution Network） is tested on CK+, JAFFE, FER2013, CHD2018 and other databases, and compared with the existing models. The highest recognition rate reaches 99.34% by GaAeS-net, which proves that the model has a good recognition rate and generalization.

Key words: expression recognition, Gabor kernel, convolutional neural network, automatic encoder, genetic algorithm, Support Vector Machine（SVM）, parameter optimization