Abstract
Behavioral biometrics is one of the most promising authentication technology in terms of various application scenes such as human-computer interaction and context-aware computing. Especially, human gait is one of the most popular trait among behavioral biometrics. Recently front-viewed gait verification has been becoming important technology because it has some advantages; human-compatible ability to identify his/her acquaintance and less installation condition of camera. It is significant to develop the technology with effective use of the advantage of front view gait so that we can develop an interactive robot with person authentication ability and a personal adaptive system with high security. In this study we develop a novel person verification method by soft biometric features observed from gait, face and body in frontal view. The facial and the body measurements, head movement time series derived from gait are independently evaluated by DP matching and Euclidean distance, and they are integrated by AND/OR calculation or fuzzy inference. As the result of verification experiment with seven subjects data, 0% false acceptance error in average can be achieved when false rejection error is 0%.
