NDLI: Machine learning methods for fully automatic recognition of facial expressions and facial actions

Content Provider	IEEE Xplore Digital Library
Author	Bartlett, M.S. Littlewort, G. Lainscsek, C. Fasel, I. Movellan, J.
Copyright Year	2004
Description	Author affiliation: Inst. for Neural Comput., California Univ., San Diego, CA, USA (Bartlett, M.S.; Littlewort, G.; Lainscsek, C.; Fasel, I.; Movellan, J.)
Abstract	We present a systematic comparison of machine learning methods applied to the problem of fully automatic recognition of facial expressions. We explored recognition of facial actions from the facial action coding system (FACS), as well as recognition of fall facial expressions. Each video-frame is first scanned in real-time to detect approximately upright frontal faces. The faces found are scaled into image patches of equal size, convolved with a bank of Gabor energy filters, and then passed to a recognition engine that codes facial expressions into 7 dimensions in real time: neutral, anger, disgust, fear, joy, sadness, surprise. We report results on a series of experiments comparing recognition engines, including AdaBoost, support vector machines, linear discriminant analysis, as well as feature selection techniques. Best results were obtained by selecting a subset of Gabor filters using AdaBoost and then training support vector machines on the outputs of the filters selected by AdaBoost. The generalization performance to new subjects for recognition of full facial expressions in a 7-way forced choice was 93% correct, the best performance reported so far on the Cohn-Kanade FACS-coded expression dataset. We also applied the system to fully automated facial action coding. The present system classifies 18 action units, whether they occur singly or in combination with other actions, with a mean agreement rate of 94.5% with human FACS codes in the Cohn-Kanade dataset. The outputs of the classifiers change smoothly as a function of time and thus can be used to measure facial expression dynamics.
Starting Page	592
Ending Page	597
File Size	525625
Page Count	6
File Format	PDF
ISBN	0780385667
ISSN	1062922X
DOI	10.1109/ICSMC.2004.1398364
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-10-10
Publisher Place	Netherlands
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Learning systems Face recognition Face detection Gabor filters Engines Support vector machines Image recognition Linear discriminant analysis Humans Time measurement
Content Type	Text
Resource Type	Article

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