Facial expression recognition in image sequences using geometric deformation features and support vector machines
Kotsia, Irene and Pitas, Ioannis (2007) Facial expression recognition in image sequences using geometric deformation features and support vector machines. IEEE Transactions on Image Processing, 16 (1). pp. 172-187. ISSN 1057-7149
Full text is not in this repository.
Official URL: http://dx.doi.org/10.1109/TIP.2006.884954
This item is available in the Library Catalogue
In this paper, two novel methods for facial expression recognition in facial image sequences are presented. The user has to manually place some of Candide grid nodes to face landmarks depicted at the first frame of the image sequence under examination. The grid-tracking and deformation system used, based on deformable models, tracks the grid in consecutive video frames over time, as the facial expression evolves, until the frame that corresponds to the greatest facial expression intensity. The geometrical displacement of certain selected Candide nodes, defined as the difference of the node coordinates between the first and the greatest facial expression intensity frame, is used as an input to a novel multiclass Support Vector Machine (SVM) system of classifiers that are used to recognize either the six basic facial expressions or a set of chosen Facial Action Units (FAUs). The results on the Cohn-Kanade database show a recognition accuracy of 99.7% for facial expression recognition using the proposed multiclass SVMs and 95.1% for facial expression recognition based on FAU detection.
|Keywords (uncontrolled):||Candide grid , Facial Action Coding S (FACS) , Facial Action Unit (FAU) , Support Vector Machines (SVMs) , facial expression recognition , machine vision , pattern recognition|
|Research Areas:||A. > School of Science and Technology > Computer Science|
A. > School of Science and Technology > Computer Science > Intelligent Environments group
|Deposited On:||19 Nov 2012 07:31|
|Last Modified:||18 Mar 2015 16:03|
Repository staff only: item control page
Full text downloads (NB count will be zero if no full text documents are attached to the record)
Downloads per month over the past year