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<p><b>Abstract</b>—The Facial Action Coding System (FACS) [<ref type="bib" rid="bibI097423">23</ref>] is an objective method for quantifying facial movement in terms of component actions. This system is widely used in behavioral investigations of emotion, cognitive processes, and social interaction. The coding is presently performed by highly trained human experts. This paper explores and compares techniques for automatically recognizing facial actions in sequences of images. These techniques include analysis of facial motion through estimation of optical flow; holistic spatial analysis, such as principal component analysis, independent component analysis, local feature analysis, and linear discriminant analysis; and methods based on the outputs of local filters, such as Gabor wavelet representations and local principal components. Performance of these systems is compared to naive and expert human subjects. Best performances were obtained using the Gabor wavelet representation and the independent component representation, both of which achieved 96 percent accuracy for classifying 12 facial actions of the upper and lower face. The results provide converging evidence for the importance of using local filters, high spatial frequencies, and statistical independence for classifying facial actions.</p>
Computer vision, facial expression recognition, independent component analysis, principal component analysis, Gabor wavelets, Facial Action Coding System.
Gianluca Donato, Terrence J. Sejnowski, Joseph C. Hager, Paul Ekman, Marian Stewart Bartlett, "Classifying Facial Actions", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 21, no. , pp. 974-989, October 1999, doi:10.1109/34.799905
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