Facial Emotion Recognition Method Based on Convolutional Neural Network

Abstract

Facial emotion recognition has poor robustness and low recognition accuracy in complex lighting, posture changes, and occlusion scenes. This study aims to design a high-performance convolutional neural network (CNN) model to improve the recognition accuracy and generalization ability of seven basic emotions in complex environments. FER2013, CK+ and Japanese female cultural specific expression (JAFFE) datasets are selected, and data preprocessing is performed through grayscale, histogram equalization and size normalization; secondly, random rotation, horizontal flipping and brightness perturbation are used for data enhancement to improve the generalization of the model; then, a 12-layer CNN model is constructed, including four convolutional blocks, two fully connected layers and an output layer, and Dropout (0.5) is used to prevent overfitting; the Adam optimizer is used to iterate 100 epochs on the training data, with cross entropy as the loss function, and the early stopping mechanism is used to optimize the hyperparameters on the validation set. The highest accuracy rate reaches 99.2% on the FER2013 test set, and the average accuracy rates of 97.3% and 88.3% are obtained in the cross-dataset tests of CK+ and JAFFE, respectively. Key performance indicators show that the average recall rate is 90.7%; the precision rate is 90.4%; the F1-score is 90.5%; the accuracy rate is still 85.2% in the standard mask occlusion test scenario. The proposed CNN model significantly improves the accuracy and robustness of emotion recognition under complex conditions through end-to-end feature learning and data enhancement strategies, providing an effective technical solution for real-time emotion analysis systems.