Dynamical personal authentication method based on natural facial movements

抄録

In this paper, we investigate a dynamical personal authentication method based on natural facial expression changes. Fingerprint authentication and conventional face authentication are static pattern-based methods, and their vulnerability to imitation using artificial objects is a significant security concern. In contrast, it is difficult to imitate human dynamic characteristics, and incorporating dynamic information into face authentication can lead to a more robust authentication method. Therefore, the purpose of this paper is to realize a dynamical personal authentication method based on natural facial expression changes. We generated difference data of facial feature vectors between two frame images constituting a video, and by using vector components in which statistically significant differences were observed, personal classification was possible with an average accuracy of 97.73%. Furthermore, using the best-performing model as the authentication module, we conducted dynamical personal authentication experiments and achieved FAR = 0% for 7 out of 13 subjects.