A Novel Facial Expression Intelligent Recognition Method Using Improved Convolutional Neural Network

Human facial expression is the core carrier of feedback. Facial expression recognition(FER) has been introduced into mickle fields, such as auxiliary medical care, safe driving, marketing assistance, distance education. However, in the real production process, facial expression image samples collected in different scenarios have problems such as complex backgrounds, which causes the FER model to train very slowly, low recognition rate, and insufficient generalization, so it cannot meet the actual production requirements. As the originator of the clustering algorithm, fuzzy C-means clustering(FCM) algorithm has stable performance and good results. It is applied to the convolutional layer of a convolutional neural network(CNN) to obtain a convolution kernel with an initial value, so as to extract the expression image features in the training set and the test set. This can solve the problem of random initialization of the convolution kernel. Based on the CNN, this paper introduces FCM to optimize the feature extraction (FE) capability of the model, and proposes a novel FER algorithm using an improved CNN(F-CNN). Because traditional CNN has problems such as irrational layer settings and too many parameters. The proposed F-CNN first adjusts the CNN network structure to improve the nonlinear expression ability of CNN. Then, replace the Softmax classifier that comes with CNN with a support vector machine (SVM) to improve the model's classification ability. The comparison experiments with other models show that the improved model improve the FER rate. The introduced FCM algorithm can effectively improve the model's FE performance and shorten the time of F-CNN during training. On the whole, F-CNN has reference value.