Emotions are individual traits for different sort of people especially for deprivation in the society, age long marginalization, inaccessibility of basic infrastructures, poverty etc. The vulnerabilities among tribal students can be understood and analyzed using the sophisticated technologies. Face detection and recognition of ahuman is an essential move in deep learning framework. Human emotion recognition assumes to be vital in finding out the vulnerabilities of individuals.The automatic identification of emotions had been a consistent research theme from early time periods .There has been a few advancements made in this fieldusing automatic identification of face emotion. Feelings are reflected from facial expressions, hand and signals of the body and through outward appearances. Thus understanding and extraction of these emotions have a high significance in the collaboration among human and machine correspondence.The paper aims to detect face emotions and annotates the every human in a given video using a combination of computational analysis, manual annotation, and experimental validation. The technique can be applicable for Tribal school kids for understanding the emotions. The system can recognize the emotion of kids and can authenticate the person.It can also be used in detecting and annotating unauthorized entries in restricted areas. Through the gaming platforms it is easy to attract the tribal students and to analyze their emotions and plan the developmental aspects based on the outcome. It will also help to understand the specific reasons of school dropouts and other issues students in tribal community face. Teachers can develop a framework using the reportfrom the application to improve the educational qualities of students. It can be concluded that Deep learning architecture-Convolution Neural Network is able to accurately classify the emotions.
Dr. Senthil Kumar T., S, R., and Rajevan, T., “Deep Learning based Emotion Analysis Approach for Strengthening Teaching-Learning process in Schools of Tribal Regions”, Journal of Advanced Research in Dynamical and Control Systems, vol. 11, pp. 621-635, 2019.