Emotion Recognition Through Computer Vision: Challenges, Techniques, and Applications

This study presents a comprehensive analysis of methods, techniques, and technologies employed in facial emotion recognition in human-robot interaction (HRI). A total of 124 articles were reviewed, selected based on specific criteria, resulting in 80 relevant studies for analysis. Techniques such as CNNs, Transfer Learning, and ViT stand out for their effectiveness, while approaches like HOG and LBP remain relevant in lowcost scenarios. Widely used datasets, such as FER-2013 and CK+, demonstrate robustness, and hardware solutions range from highperformance setups to accessible options like Google Colab. The study concludes by highlighting the importance of multimodal techniques and the potential of approaches such as ViT and MTL, proposing strategies to overcome variability challenges and adapt to real classroom conditions.