Full text
Preview |
PDF
- Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (872kB) | Preview |
Buenaposada Biencinto, José Miguel and Muñoz, Enrique and Baumela Molina, Luis (2008). Recognising facial expressions in video sequences. "Pattern Analysis and Applications", v. 11 (n. 1); pp. 101-116. ISSN 1433-7541. https://doi.org/10.1007/s10044-007-0084-8.
Title: | Recognising facial expressions in video sequences |
---|---|
Author/s: |
|
Item Type: | Article |
Título de Revista/Publicación: | Pattern Analysis and Applications |
Date: | January 2008 |
ISSN: | 1433-7541 |
Volume: | 11 |
Subjects: | |
Faculty: | Facultad de Informática (UPM) |
Department: | Inteligencia Artificial |
Creative Commons Licenses: | None |
Preview |
PDF
- Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (872kB) | Preview |
We introduce a system that processes a sequence of images of a front-facing human face and recognises a set of facial expressions. We use an efficient appearance-based face tracker to locate the face in the image sequence and estimate the deformation of its non-rigid components. The tracker works in real-time. It is robust to strong illumination changes and factors out changes in appearance caused by illumination from changes due to face deformation. We adopt a model-based approach for facial expression recognition. In our model, an image of a face is represented by a point in a deformation space. The variability of the classes of images associated to facial expressions are represented by a set of samples which model a low-dimensional manifold in the space of deformations. We introduce a probabilistic procedure based on a nearest-neighbour approach to combine the information provided by the incoming image sequence with the prior information stored in the expression manifold in order to compute a posterior probability associated to a facial expression. In the experiments conducted we show that this system is able to work in an unconstrained environment with strong changes in illumination and face location. It achieves an 89\% recognition rate in a set of 333 sequences from the Cohn-Kanade data base.
Item ID: | 1863 |
---|---|
DC Identifier: | https://oa.upm.es/1863/ |
OAI Identifier: | oai:oa.upm.es:1863 |
DOI: | 10.1007/s10044-007-0084-8 |
Deposited by: | profesor Luis Baumela Molina |
Deposited on: | 20 Oct 2009 06:38 |
Last Modified: | 20 Apr 2016 07:03 |