TY - GEN
T1 - Facial expression recogntion in unconstrained environment
AU - Hernandez-Matamoros, Andres
AU - Nagai, Takayuki
AU - Attamimi, Muhammad
AU - Nakano, Mariko
AU - Perez-Meana, Hector
N1 - Publisher Copyright:
© 2017 The authors and IOS Press. All rights reserved.
PY - 2017
Y1 - 2017
N2 - The facial expression recognition has been a topic of active researches given a result the proposal of several efficient algorithms; however, in most cases they remain limited to controlled conditions situations. In this study, we tackle the challenge of recognizing emotions through the facial expression into activities inthe- wild adding the accuracy rate for each expression. To this end we an algorithm that allows accurate face expression recognition in an uncontrolled environment, that means different kind of illumination, backgrounds, occlusions, face's profiles, etc. Proposed system firstly detects different profile of face (left, frontal and right), Then it uses only the frames in which the face profile is frontal, in the next step the face regions of interest (ROI) are segmented automatically to carry out the feature extraction. We use a classifier based on clustering, it has the advantage that if a new class (emotion) is added, it is not necessary to train this completely. Proposed system was evaluated using short video clips of several pictures together with description sentences describing the main activity in the video. The evaluation results show that the proposed scheme is able to recognize the face's profiles with the recognition rate to approximately 93% and principal emotions in unconstrained video sequences.
AB - The facial expression recognition has been a topic of active researches given a result the proposal of several efficient algorithms; however, in most cases they remain limited to controlled conditions situations. In this study, we tackle the challenge of recognizing emotions through the facial expression into activities inthe- wild adding the accuracy rate for each expression. To this end we an algorithm that allows accurate face expression recognition in an uncontrolled environment, that means different kind of illumination, backgrounds, occlusions, face's profiles, etc. Proposed system firstly detects different profile of face (left, frontal and right), Then it uses only the frames in which the face profile is frontal, in the next step the face regions of interest (ROI) are segmented automatically to carry out the feature extraction. We use a classifier based on clustering, it has the advantage that if a new class (emotion) is added, it is not necessary to train this completely. Proposed system was evaluated using short video clips of several pictures together with description sentences describing the main activity in the video. The evaluation results show that the proposed scheme is able to recognize the face's profiles with the recognition rate to approximately 93% and principal emotions in unconstrained video sequences.
KW - Face dimension estimation
KW - Face expression recognition
KW - Modal analysis
KW - Profile estimation
KW - Projective integral
UR - http://www.scopus.com/inward/record.url?scp=85029232181&partnerID=8YFLogxK
U2 - 10.3233/978-1-61499-800-6-525
DO - 10.3233/978-1-61499-800-6-525
M3 - Contribución a la conferencia
T3 - Frontiers in Artificial Intelligence and Applications
SP - 525
EP - 538
BT - New Trends in Intelligent Software Methodologies, Tools and Techniques - Proceedings of the 16th International Conference, SoMeT 2017
A2 - Fujita, Hamido
A2 - Selamat, Ali
A2 - Omatu, Sigeru
PB - IOS Press BV
T2 - 16th International Conference on New Trends in Intelligent Software Methodology Tools, and Techniques, SoMeT 2017
Y2 - 26 September 2017 through 28 September 2017
ER -