Abstract
Emotion recognition in digital images, based on the facial expressions of people, can add value in different areas such as education, shopping centers, hotels, entertainment centers, restaurants, among others, since it allows a better understanding of the requirements of the people, improve services, and predict sales trends. In a classroom, this technology allows to identify in real time the reaction of students to the development of the class, and in this way, the teacher can make the necessary adjustments to improve the learning process. The first step for this application is to detect faces of multiple students present in the scene, with efficient algorithms that process good-quality images. In this paper, the performance of six face-detection algorithms is determined using images taken in a classroom, in the town of Túquerres, in the department of Nariño, Colombia. The results show that a good camera resolution of 5 megapixels or higher, and good lighting conditions are determinant for successful face detection in classrooms of approximately 46 m2. In addition, the best performance was obtained with RetinaFace algorithm, which is more robust to different facial postures, achieving an accuracy of 96.5% with poor lighting conditions and 97.84% with good lighting conditions.
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Díaz-Toro, A. et al. (2023). Performance Evaluation of Face Detection Algorithms for an Emotion Recognition Application in a School in the Department of Nariño - Colombia. In: Bravo, J., Urzáiz, G. (eds) Proceedings of the 15th International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2023). UCAmI 2023. Lecture Notes in Networks and Systems, vol 842. Springer, Cham. https://doi.org/10.1007/978-3-031-48642-5_2
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