Automatic Evaluation of Motion Picture Contents by Estimation of Facial Expression Intensity - Sun, Yupeng; Nomiya, Hiroki; Hochin, Teruhisa (2019 20th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), 2019)
The format of the video evaluation questionnaire is varied. The user can search high-evaluation videos by referring to the video evaluation, and the video producer is motivated to produce the video by getting a good evaluation, and by analyzing the evaluation, they can get clues to make a better video. However, the evaluation can be affected by several elements which are not directly related to the video itself. In addition, not all of the users evaluate the video because the evaluation is voluntary. Hence, it is difficult to obtain accurate video evaluation. This research focuses on the importance of the fun of the video in evaluating the video. We calculate the degree of smile from the face of the user at the time of viewing the video by the expression intensity estimation, and use it to evaluate the video automatically. First, smiley face images and the expression intensity of the face image are obtained by subject experiments. Using this data, an expression intensity estimation model by a support vector machine is created. Next, the expression intensity is calculated for each frame from the face video of the person at the time of watching the video, and it is linked to the evaluation on the video. As the performance evaluation, the video evaluation obtained through the questionnaire is compared with one obtained by the proposed method. The comparison result indicates that the expression intensity is useful for the video evaluation.