Marian Stewart Bartlett, Javier R. Movellan, Gwen Littlewort, Bjorn Braathen, Mark G. Frank, and Terrence J. Sejnowski
- Published in print:
- 2005
- Published Online:
- March 2012
- ISBN:
- 9780195179644
- eISBN:
- 9780199847044
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195179644.003.0019
- Subject:
- Psychology, Cognitive Psychology
This chapter presents an approach for developing a fully automatic Facial Action Coding System (FACS). The approach uses state-of-the-art machine learning techniques that can be applied to ...
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This chapter presents an approach for developing a fully automatic Facial Action Coding System (FACS). The approach uses state-of-the-art machine learning techniques that can be applied to recognition of any facial action. The results of Study I provided guidance as to which image representations, or feature extraction methods, are most effective for facial action recognition. Gabor wavelets and Independent Component Analysis gave best performance. Study II found that machine learning techniques applied directly to the warped images is a promising approach for automatic coding of spontaneous facial expressions. Generally, the data employed hand-labeled feature points for the head pose tracking step. Furthermore, three of the issues are discussed in detail: (1) collection of a database of spontaneous facial expressions, (2) fully automatic face detection and tracking, and (3) fully automatic 3D head pose estimation.Less
This chapter presents an approach for developing a fully automatic Facial Action Coding System (FACS). The approach uses state-of-the-art machine learning techniques that can be applied to recognition of any facial action. The results of Study I provided guidance as to which image representations, or feature extraction methods, are most effective for facial action recognition. Gabor wavelets and Independent Component Analysis gave best performance. Study II found that machine learning techniques applied directly to the warped images is a promising approach for automatic coding of spontaneous facial expressions. Generally, the data employed hand-labeled feature points for the head pose tracking step. Furthermore, three of the issues are discussed in detail: (1) collection of a database of spontaneous facial expressions, (2) fully automatic face detection and tracking, and (3) fully automatic 3D head pose estimation.
