SMaTE: A Segment-Level Feature Mixing and Temporal Encoding Framework for Facial Expression Recognition.

Despite advanced machine learning methods, the implementation of emotion recognition systems based on real-world video content remains challenging. Videos may contain data such as images, audio, and text. However, the application of multimodal models using two or more types of data to real-world video media (CCTV, illegally filmed content, etc.) lacking sound or subtitles is difficult. Although facial expressions in image sequences can be utilized in emotion recognition, the diverse identities of individuals in real-world content limits computational models of relationships between facial expressions. This study proposed a transformation model which employed a video vision transformer to focus on facial expression sequences in videos. It effectively understood and extracted facial expression information from the identities of individuals, instead of fusing multimodal models. The design entailed capture of higher-quality facial expression information through mixed-token embedding facial expression sequences augmented via various methods into a single data representation, and comprised two modules: spatial and temporal encoders. Further, temporal position embedding, focusing on relationships between video frames, was proposed and subsequently applied to the temporal encoder module. The performance of the proposed algorithm was compared with that of conventional methods on two emotion recognition datasets of video content, with results demonstrating its superiority.