TY - GEN
T1 - Facial expression recognition on hexagonal structure using LBP-based histogram variances
AU - Wang, Lin
AU - He, Xiangjian
AU - Du, Ruo
AU - Jia, Wenjing
AU - Wu, Qiang
AU - Yeh, Wei Chang
PY - 2011
Y1 - 2011
N2 - In our earlier work, we have proposed an HVF (Histogram Variance Face) approach and proved its effectiveness for facial expression recognition. In this paper, we extend the HVF approach and present a novel approach for facial expression. We take into account the human perspective and understanding of facial expressions. For the first time, we propose to use the Local Binary Pattern (LBP) defined on the hexagonal structure to extract local, dynamic facial features from facial expression images. The dynamic LBP features are used to construct a static image, namely Hexagonal Histogram Variance Face (HHVF), for the video representing a facial expression. We show that the HHVFs representing the same facial expression (e.g., surprise, happy and sadness etc.) are similar no matter if the performers and frame rates are different. Therefore, the proposed facial recognition approach can be utilised for the dynamic expression recognition. We have tested our approach on the well-known Cohn-Kanade AU-Coded Facial Expression database. We have found the improved accuracy of HHVF-based classification compared with the HVF-based approach.
AB - In our earlier work, we have proposed an HVF (Histogram Variance Face) approach and proved its effectiveness for facial expression recognition. In this paper, we extend the HVF approach and present a novel approach for facial expression. We take into account the human perspective and understanding of facial expressions. For the first time, we propose to use the Local Binary Pattern (LBP) defined on the hexagonal structure to extract local, dynamic facial features from facial expression images. The dynamic LBP features are used to construct a static image, namely Hexagonal Histogram Variance Face (HHVF), for the video representing a facial expression. We show that the HHVFs representing the same facial expression (e.g., surprise, happy and sadness etc.) are similar no matter if the performers and frame rates are different. Therefore, the proposed facial recognition approach can be utilised for the dynamic expression recognition. We have tested our approach on the well-known Cohn-Kanade AU-Coded Facial Expression database. We have found the improved accuracy of HHVF-based classification compared with the HVF-based approach.
KW - Action Unit
KW - Hexagonal structure
KW - Histogram Variance Face
KW - PCA
KW - SVM
UR - http://www.scopus.com/inward/record.url?scp=78751675104&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-17829-0_4
DO - 10.1007/978-3-642-17829-0_4
M3 - Conference contribution
AN - SCOPUS:78751675104
SN - 3642178286
SN - 9783642178283
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 35
EP - 45
BT - Advances in Multimedia Modeling - 17th International Multimedia Modeling Conference, MMM 2011, Proceedings
T2 - 17th Multimedia Modeling Conference, MMM 2011
Y2 - 5 January 2011 through 7 January 2011
ER -