TY - GEN
T1 - A model based approach for expressions invariant face recognition
AU - Riaz, Zahid
AU - Mayer, Christoph
AU - Wimmer, Matthias
AU - Beetz, Michael
AU - Radig, Bernd
PY - 2009
Y1 - 2009
N2 - This paper describes an idea of recognizing the human face in the presence of strong facial expressions using model based approach. The features extracted for the face image sequences can be efficiently used for face recognition. The approach follows in 1) modeling an active appearance model (AAM) parameters for the face image, 2) using optical flow based temporal features for facial expression variations estimation, 3) and finally applying classifier for face recognition. The novelty lies not only in generation of appearance models which is obtained by fitting active shape model (ASM) to the face image using objective functions but also using a feature vector which is the combination of shape, texture and temporal parameters that is robust against facial expression variations. Experiments have been performed on Cohn- Kanade facial expression database using 62 subjects of the database with image sequences consisting of more than 4000 images. This achieved successful face recognition rate up to 91.17% using binary decision tree (BDT), 98.6% using Bayesian Networks (BN) with 10-fold cross validation in the presence of six different facial expressions.
AB - This paper describes an idea of recognizing the human face in the presence of strong facial expressions using model based approach. The features extracted for the face image sequences can be efficiently used for face recognition. The approach follows in 1) modeling an active appearance model (AAM) parameters for the face image, 2) using optical flow based temporal features for facial expression variations estimation, 3) and finally applying classifier for face recognition. The novelty lies not only in generation of appearance models which is obtained by fitting active shape model (ASM) to the face image using objective functions but also using a feature vector which is the combination of shape, texture and temporal parameters that is robust against facial expression variations. Experiments have been performed on Cohn- Kanade facial expression database using 62 subjects of the database with image sequences consisting of more than 4000 images. This achieved successful face recognition rate up to 91.17% using binary decision tree (BDT), 98.6% using Bayesian Networks (BN) with 10-fold cross validation in the presence of six different facial expressions.
KW - Active appearance models
KW - Bayesian classifier
KW - Binary decision trees
KW - Face recognition
KW - Facial expressions recognition
UR - http://www.scopus.com/inward/record.url?scp=69949134162&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-01793-3_30
DO - 10.1007/978-3-642-01793-3_30
M3 - Conference contribution
AN - SCOPUS:69949134162
SN - 3642017924
SN - 9783642017926
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 289
EP - 298
BT - Advances in Biometrics - Third International Conference, ICB 2009, Proceedings
T2 - 3rd International Conference on Advances in Biometrics, ICB 2009
Y2 - 2 June 2009 through 5 June 2009
ER -