Ashwin, T. S., Sanda, Nihar, Timalsina, Umesh, & Biswas, Gautam. (2025). “Challenges of applying computer vision for emotion detection in educational settings: A study on bias.” In Lecture Notes in Computer Science (Vol. 15882, pp. 388-395). https://doi.org/10.1007/978-3-031-98465-5_49
Understanding students’ emotions is important for creating learning environments that adapt to their needs. Advanced computer vision models like HSEmotion and EMONET can detect emotions in real time, but their effectiveness in real classrooms is not well understood. These models are usually trained on adult faces in controlled settings, which makes them less accurate when faced with different camera angles, lighting, image quality, or skin tones. This study examined how these factors—camera angle, lighting, resolution, and skin tone—affect the accuracy and fairness of emotion detection in three different learning environments. Statistical analysis shows that these variables significantly influence how accurately the models estimate students’ emotional responses.
Fig.4 Valence comparison for different camera angles