Texture and shape information fusion for facial expression and facial action unit recognition
Kotsia, Irene and Zafeiriou, Stefanos and Pitas, Ioannis (2008) Texture and shape information fusion for facial expression and facial action unit recognition. Pattern Recognition, 41 (3). pp. 833-851. ISSN 0031-3203
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Official URL: http://dx.doi.org/10.1016/j.patcog.2007.06.026
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A novel method based on fusion of texture and shape information is proposed for facial expression and Facial Action Unit (FAU) recognition from video sequences. Regarding facial expression recognition, a subspace method based on Discriminant Non-negative Matrix Factorization (DNMF) is applied to the images, thus extracting the texture information. In order to extract the shape information, the system firstly extracts the deformed Candide facial grid that corresponds to the facial expression depicted in the video sequence. A Support Vector Machine (SVM) system designed on an Euclidean space, defined over a novel metric between grids, is used for the classification of the shape information. Regarding FAU recognition, the texture extraction method (DNMF) is applied on the differences images of the video sequence, calculated taking under consideration the neutral and the expressive frame. An SVM system is used for FAU classification from the shape information. This time, the shape information consists of the grid node coordinate displacements between the neutral and the expressed facial expression frame. The fusion of texture and shape information is performed using various approaches, among which are SVMs and Median Radial Basis Functions (MRBFs), in order to detect the facial expression and the set of present FAUs. The accuracy achieved using the Cohn-Kanade database is 92.3% when recognizing the seven basic facial expressions (anger, disgust, fear, happiness, sadness, surprise and neutral), and 92.1% when recognizing the 17 FAUs that are responsible for facial expression development.
|Keywords (uncontrolled):||Facial expression recognition; facial action unit recognition; discriminant non-negative matrix factorization; multidimensional embedding; support vector machines; radial basis functions; fusion|
|Research Areas:||A. Middlesex University Schools and Centres > School of Science and Technology > Computer Science|
A. Middlesex University Schools and Centres > School of Science and Technology > Computer Science > Intelligent Environments group
|Deposited On:||26 Nov 2012 07:07|
|Last Modified:||09 Feb 2015 16:42|
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