Fusing Deep Separable Networks for Emotion Recognition in Complex Environments

This research is dedicated to addressing the challenges of model training and application implementation in the field of traditional face emotion recognition. The proposed scheme is a lightweight facial emotion recognition system based on deep separable networks. The scheme optimizes the Xception network structure in three stages. Initially, the residual module and the deep separable module are combined in order to enhance the efficiency of feature extraction. Subsequently, global average pooling is employed in lieu of the traditional fully connected layer, thereby reducing the number of parameters. Finally, the channel information is integrated through the 2×2 convolution and the point-by-point convolution, which effectively minimizes the loss of information while retaining more valuable feature information. The solution employs the Fer2013 dataset from the Kaggle platform for training with the objective of enhancing the accuracy of emotion recognition in diverse and complex environments within a simulated real-world setting. Furthermore, the objective is to develop a lightweight model that can be deployed in practical applications. The recognition accuracy has now reached an industry-leading level. Moreover, the project has developed a face emotion recognition system that has been validated through the construction of a real-time vision system. This system is capable of completing the tasks of face detection and emotion classification in complex environments, thus providing a solid foundation for the practical application of emotion recognition technology. The source code for the proposed method will be available at https://github.com/Alex-star-arch/Fusing-Deep-Separable-Networks-for-Emotion-Recognition-in-Complex-Environments.