Hand authentication from RGB-D video based on deep neural network

Abstract

In recent years, behavioral biometrics authentication, which uses the habit of behavioral characteristics for personal authentication, has attracted attention as an authentication method with higher security since behavioral biometrics cannot mimic as fingerprint and face authentications. As the behavioral biometrics, many researches were performed on voiceprints. However, there are few authentication technologies that utilize the habits of hand and finger movements during hand gestures. Only either color images or depth images are used for hand gesture authentication in the conventional methods.In the research, therefore, we propose to find individual habits from RGB-D images of finger movements and create a personal authentication system. 3D CNN, which is a deep learning-based network, is used to extract individual habits. An Fmeasure of 0.97 is achieved when rock-paper-scissors are used as the authentication operation. An F-measure of 0.97 is achieved when the disinfection operation is used. These results show the effectiveness of using RGB-D video for personal authentication.

Privacy-Aware Gait Identification With Ultralow-Dimensional Data Using a Distance Sensor

“…Among them, one of the important application scenarios for authentication is for public facilities, as some require identifying users who need access to ensure security. A number of research and technology efforts have been made to help solve the identification problem for outdoor facilities [1]- [4]. In general, the first method is to employ explicit information that identifies the user, such as the individual's name and address.…”