Abstract:

For the problem of difficulty in balancing accuracy and real-time performance of deep learning models for intelligent cockpit driver expression recognition， an expression recognition model called EmotionNet based on attention fusion and feature enhancement network is proposed. Based on GhostNet， the model utilizes two detection branches within the feature extraction module to fuse coordinate attention and channel attention mechanisms to realize complementary attention mechanisms and all-round attention to important features. A feature enhanced neck network is established to fuse feature information of different scales. Finally， decision level fusion of feature information at different scales is achieved through the head network. In training， transfer learning and central loss function are introduced to improve the recognition accuracy of the model. In the embedded device testing experiments on the RAF-DB and KMU-FED datasets， the model achieves the recognition accuracy of 85.23% and 99.95%， respectively， with a recognition speed of 59.89 FPS. EmotionNet balances recognition accuracy and real-time performance， achieving a relatively advanced level and possessing certain applicability for intelligent cockpit expression recognition tasks.

Key words: intelligent cockpit, expression recognition, attention mechanisms, feature enhancement network