Body Motion Analysis for Multi-modal Identity Verification

This paper shows how “Body Motion Signature Analysis” - a new “soft-biometrics” technique - can be used for identity verification. It is able to extract motion features from the upper body of people and estimates so called “super-features” for input to a classifier. We demonstrate how this new technique can be used to identify people just based on their motion, or it can be used to significantly improve “hard-biometrics” techniques. For example, face verification achieves on this domain 6.45% Equal Error Rate (EER), and the combined verification performance of motion features and face reduces the error to 4.96% using an adaptive score-level integration method. The more ambiguous motion-only performance is 17.1% EER.