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In this paper we demonstrate efficient methods for continuous estimation of eye gaze angles with application to sign language videos. The difficulty of the task lies on the fact that those videos contain images with low face resolution since they are recorded from distance. First, we proceed to the modeling of face and eyes region by training and fitting Global and Local Active Appearance Models (LAAM)...
We propose a new approach for Extreme States Classification (ESC) on feature spaces of facial cues in sign language (SL) videos. The method is built upon Active Appearance Model (AAM) face tracking and feature extraction of global and local AAMs. ESC is applied on various facial cues - as, for instance, pose rotations, head movements and eye blinking - leading to the detection of extreme states such...
We propose an Unsupervised method for Extreme States Classification (UnESC) on feature spaces of facial cues of interest. The method is built upon Active Appearance Models (AAM) face tracking and on feature extraction of Global and Local AAMs. UnESC is applied primarily on facial pose, but is shown to be extendable for the case of local models on the eyes and mouth. Given the importance of facial...
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