1st International Conference on Science and Technology, ICOST 2019, 2-3 May, Makassar, Indonesia

Research Article

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

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  • @INPROCEEDINGS{10.4108/eai.2-5-2019.2284609,
        author={Muhammad Furqan Rasyid and Zahir  Zainuddin and Andani  Andani},
        title={Early Detection of Health Kindergarten Student  at School Using Image Processing Technology},
        proceedings={1st International Conference on Science and Technology, ICOST 2019, 2-3 May, Makassar, Indonesia},
        publisher={EAI},
        proceedings_a={ICOST},
        year={2019},
        month={6},
        keywords={kindergarten pca eigenface euclidean distance},
        doi={10.4108/eai.2-5-2019.2284609}
    }
    
  • Muhammad Furqan Rasyid
    Zahir Zainuddin
    Andani Andani
    Year: 2019
    Early Detection of Health Kindergarten Student at School Using Image Processing Technology
    ICOST
    EAI
    DOI: 10.4108/eai.2-5-2019.2284609
Muhammad Furqan Rasyid1,*, Zahir Zainuddin1, Andani Andani1
  • 1: Department of Electrical Engineering, Faculty of Engineering, Universitas Hasanuddin Makassar, Indonesia, 92119
*Contact email: rasyidmf17d@student.unhas.ac.id

Abstract

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%.