Human vision inspired framework for facial expressions recognition

Abstract : We present a novel human vision inspired framework that can recognize facial expressions very efficiently and accurately. We propose to computationally process small, salient region of the face to extract features as it happens in human vision. To determine which facial region(s) is perceptually salient for a particular expression, we conducted a psycho-visual experimental study with an eye-tracker. A novel feature space conducive for recognition task is proposed, which is created by extracting Pyramid Histogram of Orientation Gradients features only from the salient facial regions. By processing only salient regions, proposed framework achieved two goals: (a) reduction in computational time for feature extraction (b) reduction in feature vector dimensionality. The proposed framework achieved automatic expression recognition accuracy of 95.3% on extended Cohn-Kanade (CK+) facial expression database for six universal facial expressions.
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Rizwan Ahmed Khan, Alexandre Meyer, Hubert Konik, Saida Bouakaz. Human vision inspired framework for facial expressions recognition. Image Processing (ICIP), 2012 19th IEEE International Conference on, Sep 2012, Orlando, FL, United States. pp.2593 - 2596, ⟨10.1109/ICIP.2012.6467429⟩. ⟨hal-00794159⟩

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