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We present a systematic comparison of machine learning methods applied to the problem of fully automatic recognition of facial expressions. We report results on a series of experiments comparing recognition engines, including Ad-aBoost, support vector machines, linear discriminant analysis. We also explored feature selection techniques, including the use of(More)
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(More)
– 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 full facial expressions. Each video-frame is first scanned in real-time to detect approximately(More)
— We present the Computer Expression Recognition Toolbox (CERT), a software tool for fully automatic real-time facial expression recognition, and officially release it for free academic use. CERT can automatically code the intensity of 19 different facial actions from the Facial Action Unit Coding System (FACS) and 6 different protoypical facial(More)
We present results on a user independent fully automatic system for real time recognition of facial actions from the Facial Action Coding System (FACS). The system automatically detects frontal faces in the video stream and codes each frame with respect to 20 Action units. We present preliminary results on a task of facial action detection in spontaneous(More)
Machine learning approaches have produced some of the highest reported performances for facial expression recognition. However, to date, nearly all automatic facial expression recognition research has focused on optimizing performance on a few databases that were collected under controlled lighting conditions on a relatively small number of subjects. This(More)
Computer animated agents and robots bring a social dimension to human computer interaction and force us to think in new ways about how computers could be used in daily life. Face to face communication is a real-time process operating at a a time scale in the order of 40 milliseconds. The level of uncertainty at this time scale is considerable, making it(More)
We formulate a probabilistic model of image generation and derive optimal inference algorithms for finding objects and object features within this framework. The approach models images as a collage of patches of arbitrary size, some of which contain the object of interest and some of which are background. The approach requires development of(More)
The capacity for shared attention is a cornerstone of human social intelligence. We propose that the development of shared attention depends on a proper interaction of motivational biases and contingency learning mechanisms operating in an appropriately structured environment. Atypical contingency learning leads to deficits in shared attention as seen in(More)