Multimodal identification using Markov logic networks

Human robot interaction presents a unique set of challenges for biometric person identification. During normal interactions between the robot and a user, a tremendous amount of information is available for identification. Our objective is to use this information to identify users quickly and accurately during interactions with a robot. We present our approach for multimodal person identification using Markov logic networks (MLN). We use appearance, clothing, speaker recognition, and face recognition to identify a person during an interaction where they are speaking to the robot. We demonstrate the effectiveness of our approach using sequences of individuals speaking freely on a topic of their choosing.