Emotion Sensing From Head Motion Capture

Computational analysis of emotion from verbal and non-verbal behavioral cues is critical for human-centric intelligent systems. Among the non-verbal cues, head motion has received relatively less attention, although its importance has been noted in several research. We propose a new approach for emotion recognition using head motion captured using Motion Capture (MoCap). Our approach is motivated by the well known kinesics-phonetic analogy , which advocates that, analogous to human speech being composed of phonemes, head motion is composed of kinemes i.e., elementary motion units. We discover a set of kinemes from head motion in an unsupervised manner by projecting them onto a learned basis domain and subsequently clustering them. This transforms any head motion to a sequence of kinemes. Next, we learn the temporal latent structures within the kineme sequence pertaining to each emotion. For this purpose, we explore two separate approaches – one using Hidden Markov Model and another using neural network. This class-specific, kineme-based representation of head motion is used to perform emotion recognition on the popular IEMOCAP database. We achieve high recognition accuracy (61.8% for three class) for various emotion recognition tasks using head motion alone . This work adds to our understanding of head motion dynamics, and has applications in emotion analysis and head motion animation and synthesis.