Discriminative Regions Selection for Facial Expression Recognition
Human Machine Interaction systems are able to perceive facial
expressions more naturally and reliably. In this paper, we
introduced a new idea to recognize facial expression by selecting
the most discriminative facial regions relying on facial expression
appearance. The proposed approach is based on the prior
knowledge of psychology studies which show that only some
facial regions are descriptive in expression revelation. In fact,
regions selection seeks to collect the descriptive regions which are
responsible of expression divulgence and this was performed
using Mutual Information technique. Regarding facial feature
extraction, we applied Local Binary Pattern technique to encode
facial expression micro-patterns. An experimental study shows
that using descriptive regions improved facial expression
classification accuracy as well as reduced features vector size.
Indeed, we attested the independency of the selected regions of
the dataset and the descriptors.
Keywords: Facial Expression Recognition, Local Binary Patter (LBP), Discrete Wavelet Transform (DWT).
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ABOUT THE AUTHORS
Hazar Mliki
Hazar Mliki graduated with a master’s thesis in computer science from the University of Sfax in Tunisia. She is a researcher in the MIRACL Laboratory (Multimedia, InfoRmation systems and Advanced Computing Laboratory). Currently, she is preparing her Ph.D at the University of Sfax in Tunisia (FSEGS). Her research interests include biometrics, pattern recognition and image processing.
Mohamed Hammami
Mohamed Hammami received a Ph.D in computer science from Ecole Centrale at the Lyon Research Center for Images and Intelligent Information Systems (LIRIS) associated to the French research institution CNRS as UMR5205. He is currently associate professor in the Computer Science Department at the Faculty of Science Sfax-Tunisia. He is a researcher in the MIRACL Laboratory (Multimedia, InfoRmation systems and Advanced Computing Laboratory). His current research interests include data mining and knowledge discovery in images and video, multimedia indexing and retrieval, face detection and recognition, and Web site filtering. He was a staff member in RNTL-Muse project. He has served on technical conference committees and as reviewer in many international conferences.
Hazar Mliki
Hazar Mliki graduated with a master’s thesis in computer science from the University of Sfax in Tunisia. She is a researcher in the MIRACL Laboratory (Multimedia, InfoRmation systems and Advanced Computing Laboratory). Currently, she is preparing her Ph.D at the University of Sfax in Tunisia (FSEGS). Her research interests include biometrics, pattern recognition and image processing.
Mohamed Hammami
Mohamed Hammami received a Ph.D in computer science from Ecole Centrale at the Lyon Research Center for Images and Intelligent Information Systems (LIRIS) associated to the French research institution CNRS as UMR5205. He is currently associate professor in the Computer Science Department at the Faculty of Science Sfax-Tunisia. He is a researcher in the MIRACL Laboratory (Multimedia, InfoRmation systems and Advanced Computing Laboratory). His current research interests include data mining and knowledge discovery in images and video, multimedia indexing and retrieval, face detection and recognition, and Web site filtering. He was a staff member in RNTL-Muse project. He has served on technical conference committees and as reviewer in many international conferences.
