Multi-view multi-modal person authentication from a single walking image sequence

This paper describes a method for multi-view multimodal biometrics from a single walking image sequence. As multi-modal cues, we adopt not only face and gait but also the actual height of a person, all of which are simultaneously captured by a single camera. As multi-view cues, we use the variation in the observation views included in a single image sequence captured by a camera with a relatively wide field of view. This enables us to improve the authentication of a person based on multiple modalities and views, while retaining the potential for real applications such as surveillance and forensics using only a single image sequence (a single session with a single camera). In the experiments, we constructed a large-scale multi-view multimodal score data set with 1,912 subjects, and evaluated the proposed method using the data set in a statistically reliable way. We achieved 0.37% equal error rates for the false acceptance and rejection rates in the verification scenarios, and 99.15% rank-1 identification rate in the identification scenarios.