Please use this identifier to cite or link to this item: https://doi.org/10.1007/s11370-007-0014-z
Title: Facial expression recognition and tracking for intelligent human-robot interaction
Authors: Yang, Y.
Ge, S.S. 
Lee, T.H. 
Wang, C.
Issue Date: Apr-2008
Source: Yang, Y.,Ge, S.S.,Lee, T.H.,Wang, C. (2008-04). Facial expression recognition and tracking for intelligent human-robot interaction. Intelligent Service Robotics 1 (2) : 143-157. ScholarBank@NUS Repository. https://doi.org/10.1007/s11370-007-0014-z
Abstract: For effective interaction between humans and socially adept, intelligent service robots, a key capability required by this class of sociable robots is the successful interpretation of visual data. In addition to crucial techniques like human face detection and recognition, an important next step for enabling intelligence and empathy within social robots is that of emotion recognition. In this paper, an automated and interactive computer vision system is investigated for human facial expression recognition and tracking based on the facial structure features and movement information. Twenty facial features are adopted since they are more informative and prominent for reducing the ambiguity during classification. An unsupervised learning algorithm, distributed locally linear embedding (DLLE), is introduced to recover the inherent properties of scattered data lying on a manifold embedded in high-dimensional input facial images. The selected person-dependent facial expression images in a video are classified using the DLLE. In addition, facial expression motion energy is introduced to describe the facial muscle's tension during the expressions for person-independent tracking for person-independent recognition. This method takes advantage of the optical flow which tracks the feature points' movement information. Finally, experimental results show that our approach is able to separate different expressions successfully. © 2008 Springer-Verlag.
Source Title: Intelligent Service Robotics
URI: http://scholarbank.nus.edu.sg/handle/10635/55999
ISSN: 18612776
DOI: 10.1007/s11370-007-0014-z
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