ViTCN: Hybrid Vision Transformer with Temporal Convolution for Multi-Emotion Recognition

• Published in: International journal of computational intelligence systems 2024-03, Vol.17 (1), p.1-20, Article 64
• Main Authors: Zakieldin, Kamal, Khattab, Radwa, Ibrahim, Ehab, Arafat, Esraa, Ahmed, Nehal, Hemayed, Elsayed
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Computational Intelligence; Computer-vision; Control; Deep-learning; Emotion-recognition; Engineering; Mathematical Logic and Foundations; Mechatronics; Research Article; Robotics; Temporal-convolution-network; Vision-transformer
• ISSN: 1875-6883; 1875-6883
• DOI: 10.1007/s44196-024-00436-5

In Talentino, HR-Solution analyzes candidates’ profiles and conducts interviews. Artificial intelligence is used to analyze the video interviews and recognize the candidate’s expressions during the interview. This paper introduces ViTCN, a combination of Vision Transformer (ViT) and Temporal Convolution Network (TCN), as a novel architecture for detecting and interpreting human emotions and expressions. Human expression recognition contributes widely to the development of human-computer interaction. The machine’s understanding of human emotions in the real world will considerably contribute to life in the future. Emotion recognition was identifying the emotions as a single frame (image-based) without considering the sequence of frames. The proposed architecture utilized a series of frames to accurately identify the true emotional expression within a combined sequence of frames over time. The study demonstrates the potential of this method as a viable option for identifying facial expressions during interviews, which could inform hiring decisions. For situations with limited computational resources, the proposed architecture offers a powerful solution for interpreting human facial expressions with a single model and a single GPU.The proposed architecture was validated on the widely used controlled data sets CK+, MMI, and the challenging DAiSEE data set, as well as on the challenging wild data sets DFEW and AFFWild2. The experimental results demonstrated that the proposed method has superior performance to existing methods on DFEW, AFFWild2, MMI, and DAiSEE. It outperformed other sophisticated top-performing solutions with an accuracy of 4.29% in DFEW, 14.41% in AFFWild2, and 7.74% in MMI. It also achieved comparable results on the CK+ data set.