TY - GEN
T1 - Transductive transfer LDA with riesz-based volume LBP for Emotion recognition in the wild
AU - Zong, Yuan
AU - Zheng, Wenming
AU - Huang, Xiaohua
AU - Yan, Jingwei
AU - Zhang, Tong
N1 - Publisher Copyright:
© 2015 ACM.
PY - 2015/11/9
Y1 - 2015/11/9
N2 - In this paper, we propose the method using Transductive Transfer Linear Discriminant Analysis (TTLDA) and Rieszbased Volume Local Binary Patterns (RVLBP) for image based static facial expression recognition challenge of the Emotion Recognition in the Wild Challenge (EmotiW 2015). The task of this challenge is to assign facial expression labels to frames of some movies containing a face under the real word environment. In our method, we firstly employ a multi-scale image partition scheme to divide each face image into some image blocks and use RVLBP features extracted from each block to describe each facial image. Then, we adopt the TTLDA approach based on RVLBP to cope with the expression recognition task. The experiments on the testing data of SFEW 2.0 database, which is used for image based static facial expression challenge, demonstrate that our method achieves the accuracy of 50%. This result has a 10.87% improvement over the baseline provided by this challenge organizer.
AB - In this paper, we propose the method using Transductive Transfer Linear Discriminant Analysis (TTLDA) and Rieszbased Volume Local Binary Patterns (RVLBP) for image based static facial expression recognition challenge of the Emotion Recognition in the Wild Challenge (EmotiW 2015). The task of this challenge is to assign facial expression labels to frames of some movies containing a face under the real word environment. In our method, we firstly employ a multi-scale image partition scheme to divide each face image into some image blocks and use RVLBP features extracted from each block to describe each facial image. Then, we adopt the TTLDA approach based on RVLBP to cope with the expression recognition task. The experiments on the testing data of SFEW 2.0 database, which is used for image based static facial expression challenge, demonstrate that our method achieves the accuracy of 50%. This result has a 10.87% improvement over the baseline provided by this challenge organizer.
KW - Emotion recognition in the wild 2015
KW - Facial expression recognition
KW - Reisz-based volume local binary pattern
KW - Transductive Transfer Linear Discriminant Analysis
UR - http://www.scopus.com/inward/record.url?scp=84959298618&partnerID=8YFLogxK
U2 - 10.1145/2818346.2830584
DO - 10.1145/2818346.2830584
M3 - Conference contribution
AN - SCOPUS:84959298618
T3 - ICMI 2015 - Proceedings of the 2015 ACM International Conference on Multimodal Interaction
SP - 491
EP - 496
BT - ICMI 2015 - Proceedings of the 2015 ACM International Conference on Multimodal Interaction
PB - Association for Computing Machinery, Inc
T2 - ACM International Conference on Multimodal Interaction, ICMI 2015
Y2 - 9 November 2015 through 13 November 2015
ER -