Identifying People based on their Motion Signature

Introduction In the past, using motion for recognition has been demonstrated mainly for classifying certain actions or gait styles ([1, 2, 3, 4, 5]). Using the more ambiguous motion of a person that appears during talking and performing “body language” is harder to quantify. This is similar to what the acoustic speech community calls “speaker recognition”. It is not important what the person says, but how it is said. We developed a new video-based feature extraction technique and methods to train statistical models that classify body motion signatures. The recognition architecture is inspired by recent progress in speaker recognition research. This abstract has 3 main contributions: 1) a visual feature estimation technique, based on sparse flow computations and motion angle histograms that we call “Motion Orientation Signatures” (MOS). 2) Integration of this feature into a 3-stage recognition system, 3) an integration method with a state-of-the-art face recognition architecture [6]. 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. This is to the best of our knowledge the first time a system has been demonstrated that can significantly improve a state-of-the-art face recognition system with such complex and ambiguous signals like a person’s body-language.