Phankokkruad, Manop

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Phankokkruad, Manop
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manop.ph@kmitl.ac.th

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    Item type:Publication,
    A real-time face recognition for class participation enrollment system over WebRTC
    (2016-01-01)
    Phankokkruad, Manop
    ;
    Jaturawat, Phichaya
    ;
    Pongmanawut, Pasinee
    In the classroom, students can get the most benefit for themselves when attend and participate in the classroom. Roll-call is a classical method that mostly uses for the class participation enrollment. The time that used for this method is depended on the number of students; the more number of students, the more time to spend. This work presents the method that improves the class participation enrollment process Thus, we developed the face detection and face recognition system by applying the WebRTC. Since it is a platform independent, we could capture the participant faces from anywhere without an installation. In addition, the three standard face detection and recognition algorithms were applied in two main processes properly. The result showed that system can improve the class participation enrollment accuracy to be more precise and persuaded the student to attend the class as well. Moreover, the system can install to the classroom easily because it is developed in form of the web application and needs an only web camera for the additional device.
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    Item type:Publication,
    Influence of facial expression and viewpoint variations on face recognition accuracy by different face recognition algorithms
    (2017-08-29)
    Phankokkruad, Manop
    ;
    Jaturawat, Phichaya
    Face recognition is a personal identification method using biometrics that is gaining the attention in this research field. The face recognition process can be done without the human and devices interaction, so it can be applied in several applications. In additions, the face recognition systems are typically implemented at different places in unconstrained environments. Hence, the study of the factors that impact the face recognition accuracy is an interesting and challenging topic. In the class attendance checking system using face recognition, there are variations of three factors that possibly affect the accuracy of the system; facial expressions, and face viewpoints. This study intends to compare facial recognition accuracy of three well-known algorithms namely Eigenfaces, Fisherfaces, and LBPH. The experiments conducted in the respects of the variation of facial expressions, and face viewpoints in the actual classroom. The results of the experiment demonstrated that LBPH is the most precise algorithm which achieves 81.67% of accuracy in still-image-based testing. The facial expression that has the most impact on accuracy is the grin, and face viewpoints that affect accuracy are looking down and tilting left, and right respectively. Therefore, LBPH is the most suitable algorithm to apply in a class attendance checking system after considering the accuracy.