Facial Expression Recognition Algorithm Based on the Combination of CNN and K-Means

Aiming at the problems of low recognition rate and slow training speed of facial expression recognition method in the background of complex images, an improved facial expression recognition algorithm based on convolutional neural networks is proposed. The proposed algorithm introduces K-Means clustering idea and SVM classifier in the framework of convolutional neural network. Firstly, the algorithm trains the K-Means clustering model by using the label-free expression images, and selects the K-means clustering centers with good data characteristics, which are used as the initial value of the convolution kernel of the CNN model to extract features. Secondly, using the feature extraction processing of the convolutional neural network, the extracted features are fed to the multi-class SVM classifier. The experimental results show that the proposed method reduces the training time of the model overall, improves the accuracies of facial expression recognition under the background of complex images, and has a certain robustness.