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Dynamics of facial expression extracted automatically from video
2006
Image and Vision Computing
We present a systematic comparison of machine learning methods applied to the problem of fully automatic recognition of facial expressions, including AdaBoost, support vector machines, and linear discriminant analysis. 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
doi:10.1016/j.imavis.2005.09.011
fatcat:4f4zxodagrh4lbuy5nyom5bzdm