Unsupervised Discovery of Facial Events

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Abstract

Automatic facial image analysis has been a long stand- ing research problem in computer vision. A key component in facial image analysis, largely conditioning the success of subsequent algorithms (e.g. facial expression recognition), is to define a vocabulary of possible dynamic facial events. To date, that vocabulary has come from the anatomically- based Facial Action Coding System (FACS) or more subjec- tive approaches (i.e. emotion-specified expressions). The aim of this paper is to discover facial events directly from video of naturally occurring facial behavior, without re- course to FACS or other labeling schemes. To discover facial events, we propose a temporal clustering algorithm, Aligned Cluster Analysis (ACA), and a multi-subject cor- respondence algorithm for matching expressions. We use a variety of video sources: posed facial behavior (Cohn- Kanade database), unscripted facial behavior (RU-FACS database) and some video in infants. Accuracy of (unsu- pervised) ACA approached that of a supervised version, achieved moderate intersystem agreement with FACS, and proved informative as a visualization/summarization tool.

Citation

	Feng Zhou, Fernando de la Torre, Jeffrey F. Cohn and Tomas Simon, "Unsupervised Discovery of Facial Events", CMU Technical Report TR-10-10, 2010. [PDF] [Bibtex]
	Feng Zhou, Fernando de la Torre and Jeffrey F. Cohn, "Unsupervised Discovery of Facial Events", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010. [PDF] [Bibtex]

Code

here

Acknowledgements and Funding

This work was partially supported by National Institute of Health Grant R01 MH 051435. Thanks to Tomas Simon and Zaid Harchaoui for helpful discussions, and Iain Matthews and Simon Baker for assistance with the AAM code.

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