TY - GEN
T1 - High dimensional local binary patterns for facial expression recognition in the wild
AU - Radlak, Krystian
AU - Smolka, Bogdan
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/6/20
Y1 - 2016/6/20
N2 - In this paper, we propose an automatic method of facial expressions recognition in static images using the high dimensional Local Binary Patterns (LBP). In this research some existing algorithms for face detection, facial landmarks localization, face normalization and recognition were combined and adopted for facial expressions classification. The novelty of the contribution lies in the application of the Random Frog algorithm used in the gene selection in the microarray experiments together with the high dimensional LBP as features of the Support Vector Machines (SVM) classifier used for the facial expressions classification. The proposed method was evaluated on the Static Facial Expressions in the Wild (SFEW) database, which contains the face images coming from movies and is very similar to real world scenarios. The performed tests show that the high dimensional LBP and features calculated from the regions around facial landmarks can achieve significant improvements over the state-of-the-art methods.
AB - In this paper, we propose an automatic method of facial expressions recognition in static images using the high dimensional Local Binary Patterns (LBP). In this research some existing algorithms for face detection, facial landmarks localization, face normalization and recognition were combined and adopted for facial expressions classification. The novelty of the contribution lies in the application of the Random Frog algorithm used in the gene selection in the microarray experiments together with the high dimensional LBP as features of the Support Vector Machines (SVM) classifier used for the facial expressions classification. The proposed method was evaluated on the Static Facial Expressions in the Wild (SFEW) database, which contains the face images coming from movies and is very similar to real world scenarios. The performed tests show that the high dimensional LBP and features calculated from the regions around facial landmarks can achieve significant improvements over the state-of-the-art methods.
UR - https://www.scopus.com/pages/publications/84979284368
U2 - 10.1109/MELCON.2016.7495381
DO - 10.1109/MELCON.2016.7495381
M3 - Conference contribution
AN - SCOPUS:84979284368
T3 - Proceedings of the 18th Mediterranean Electrotechnical Conference: Intelligent and Efficient Technologies and Services for the Citizen, MELECON 2016
BT - Proceedings of the 18th Mediterranean Electrotechnical Conference
A2 - Mavromoustakis, Constandinos
A2 - Louca, Soulla
A2 - Pattichis, Constantinos S.
A2 - Georgiou, Julius
A2 - Michael, Despina
A2 - Paschalidou, A.
A2 - Kyriacou, Efthyvoulos
A2 - Vassiliou, Vasos
A2 - Panayiotou, Christos
A2 - Kyriakides, Elias
A2 - Ellinas, Georgios
A2 - Hadjichristofi, George
A2 - Loizou, C.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th Mediterranean Electrotechnical Conference, MELECON 2016
Y2 - 18 April 2016 through 20 April 2016
ER -