Early Detection of Health Kindergarten Student at School Using Image Processing Technology

The health early detection system for kindergarten students helps teachers to monitor the health conditions of students. For this reason, the purpose of this system is to propose a system that can detect the health of kindergarten students so that teachers can concentrate more on teaching. This paper presents a technique of combining facial recognition and movement classification for health classifications. For expression recognition, this system uses the PCA method to extract features, then the Euclidean distance algorithm is used to calculate between Eigen Face images and test images. Classification of movements, this study uses two classifications namely active and inactive. For recognition of facial expressions, this system obtains an accuracy of 83.75%.