Research and Implementation of Face Expression Recognition Algorithm Based on Deep Learning

With the continuous development of deep learning technology, facial expression recognition (FER) has made significant progress. Unlike traditional FER methods that rely on manually crafted features, deep learning enables models to automatically learn discriminative features from large-scale datasets, effectively reducing the influence of environmental noise. This study focuses on facial expression recognition using deep learning techniques, specifically by constructing a convolutional neural network (CNN) model. Techniques such as data augmentation and model optimization are employed to enhance performance. The model is trained and evaluated on large-scale facial expression datasets to accurately recognize emotions in complex scenarios. Experimental results demonstrate that the proposed deep learning-based approach outperforms traditional methods in terms of robustness and recognition accuracy. This work not only advances facial expression recognition but also provides a valuable reference for future research in affective computing and human-computer interaction.