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A key issue regarding feature extraction is the capability of a technique to extract distinctive features to represent facial expressions while requiring a low computational complexity. In this study, the authors propose a novel approach for appearance-based facial feature extraction to perform the task of facial expression recognition on video sequences. The proposed spatiotemporal texture map (STTM)...
Spatial Gabor energy filters (GE) are one of the most successful approaches to represent facial expressions in computer vision applications, including face recognition and expression analysis. It is well known that these filters approximate the response of complex cells in primary visual cortex. However these neurons are modulated by the temporal, not just spatial, properties of the visual signal...
Different facial expressions are related to a small set of muscles and limited ranges of motions. In this paper we propose an automatic facial expression recognition system, different from other automatic methods in both face detection and feature extraction. In system the facial expressions identify itself in video sequences. First, the differences between neutral and emotional states are detected...
In this paper, a novel method for the recognition of facial expressions in videos is proposed. The system first extracts the deformed Candide facial grid that corresponds to the facial expression depicted in the video sequence. The mean Euclidean distance of the deformed grids is then calculated to create a new metric multidimensional scaling. The classification of the sample under examination to...
A novel method based on geometrical and texture information is proposed for facial expression recognition from video sequences. The discriminant non-negative matrix factorization (DNMF) algorithm is applied at the image of the last frame of the video sequence, corresponding to the greatest intensity of the facial expression, thus extracting the texture information. A support vector machines (SVMs)...
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