Abstract -Enabling computer systems to recognize human facial expressions is a challenging research problem with many applications in behavioral science, medicine, security, a n d human-machine interaction. Instead of being another approach to automatic detection of protoQpic facial expressions of emotion, this work attempts to analyze subtle changes in facial behavior by recognizing facial action units (AUs, i.e. atomic facial signals) that produce expressions. This paper proposes AU recognition based upon multilevel motion history images (MMHIs), which can be seen as an extension to temporal templates introduced by Bobick and Davis. By recording motion history at multiple time intervals (i.e.. multilevel MHIs) instead of recording it once for the entire image sequence, we overcome the problem of self-occlusion which is inherent to temporal templates original definition. For automatic classification of an input M I -r e p r e s e n t e d face video in terms of 21 AU classes, two approaches are compared: a Sparse Network of Winnows (SNo W) a n d a standard kNearest Neighbour @A") classifer. The system was tested on hvo direrent databases, the MM-Face-DB developed by the authors a n d the Cohn-Kanadeface database.
Valstar et al described in REF a different extension of MHI, namely the multiple-level MHI (MMHI), which aims at handling motion self-occlusion by recording motion history at multiple time intervals.
Motion history for facial action detection in video
2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583)
2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583)
Computer Science