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Child Attention Detection through Facial Expression Recognition using SVM Algorithm

Determining the ability of children to focus in a very young age is something that is very important for adults to know for them to understand the child's learning capability. The development of their attention skills during their younger years affects how much excellence they can perform during their adolescence stage. Since most attention detection researches are frequently done through eye gaze detection, this research is focused on detecting the attention of a child through facial expressions. The proposed system shows that basing from a child's facial expression, it can determine their attention skills which has given accurate results. A total of forty (40) grade one (1) students took part in this research. The data gathered was in the form of a recorded video obtained from the web camera. Each video was processed frame by frame to extract necessary facial features that is needed in determining the facial expression through OpenFace application. SVM algorithm was used in training and testing the model's validity. The model is written in a Java Programming Language and has an output of a subtitle file which will be imported into the recorded video. From there, the subtitle file has a label of the student's facial expression, thus determines their attention. To determine the predictive power of the model, K-fold cross validation method was used.