Enhanced Convolutional Neural Network for Robust Facial Expression Recognition on Fer2013 and Natural Image Datasets

This study presents an enhanced convolutional neural network (CNN) architecture tailored for accurate facial expression recognition. The model is trained on the FER2013 dataset and evaluated using both FER2013 and a custom dataset containing natural facial expressions. By incorporating multiple convolutional and pooling layers along with dropout regularization, the network effectively extracts and classifies emotion-related features. Experimental results demonstrate high recognition accuracy and strong generalization across controlled and real-world image scenarios. In order to study the application of convolutional neural networks in facial expression recognition, a 10-layer convolutional neural network model is designed to recognize facial expressions. The last layer uses the Softmax function to output the classification results of expressions. First, the convolution and pooling algorithms of convolutional neural networks were studied and the structure of the model was designed. Secondly, in order to more vividly display the features extracted by the convolutional layer, the extracted features are visualized and displayed in the form of feature maps. The convolutional neural network model in this work was tested on the Fer-2013 data set, and the experimental results demonstrated the superiority of the recognition rate. It is known that the Fer-2013 dataset contains data collected in an experimental environment, and in order to verify the generalization ability of model recognition, a self-made facial expression data set in natural state was created, and performed a series of preprocessing on the face images such as cropping, grayscale and pixel adjustment. The trained model, which was previously applied to the Fer-2013 dataset, was tested out on the new dataset. The experiment yielded promising results, one of which in the form of a recognition accuracy rate as high as 85.1%.