Facial expression recognition via weighted group sparsity
Hao ZHENG1,2(),Xin GENG2
1. Key Laboratory of Trusted Cloud Computing and Big Data Analysis, School of Information Engineering, Nanjing Xiaozhuang University, Nanjing 211171, China 2. School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
Considering the distinctiveness of different group features in the sparse representation, a novel joint multitask and weighted group sparsity (JMT-WGS) method is proposed. By weighting popular group sparsity, not only the representation coefficients from the same class over their associate dictionaries may share some similarity, but also the representation coefficients from different classes have enough diversity. The proposed method is cast into a multi-task framework with two-stage iteration. In the first stage, representation coefficient can be optimized by accelerated proximal gradient method when the weights are fixed. In the second stage, the weights are computed via the prior information about their entropy. The experimental results on three facial expression databases show that the proposed algorithm outperforms other state-of-the-art algorithms and demonstrate the promising performance of the proposed algorithm.
Pantic M, Rothkrantz L J M. Automatic analysis of facial expressions: the state of the art. IEEE Transactions on Pattern Analysis andMachine Intelligence, 2000, 22(12): 1424–1445
5
De La Torre F, Chu W, Xiong X, Cohn J. Intraface. In: Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition. 2015, 1–8 https://doi.org/10.1109/fg.2015.7163082
Bartlett M S, Littlewort G, Fasel L, Movellan J R. Real time face detection and facial expression recognition: development and application to human-computer interaction. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2003 https://doi.org/10.1109/cvprw.2003.10057
8
Bourel F, Chibelushi C C, Low A A. Robust facial expression recognition using a state-based model of spatiallylocalised facial dynamic. In: Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition. 2002, 113–118 https://doi.org/10.1109/AFGR.2002.1004141
9
Tian Y I, Kanade T, Cohn J F. Recognizing action units for facial expression analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(2): 97–115 https://doi.org/10.1109/34.908962
10
Meng H Y, Bianchi-Berthouze N. Naturalistic affective expression classification by a multi-stage approach based on hidden Markov models. In: D’Mello S, Graesser A, Schuller B, et al, eds. Affective Computing and Intelligent Interaction. Lecture Notes in Computer Science, Vol 6975. Berlin: Springer, 2011, 378–387 https://doi.org/10.1007/978-3-642-24571-8_49
11
Cohen I, Sebe N, Garg A, Chen L S, Huang T S. Facial expression recognition from video sequences: temporal and static modeling. Computer Vision and Image Understanding, 2003, 91(1): 160–187 https://doi.org/10.1016/S1077-3142(03)00081-X
12
Kotsia I, Pitas I. Facial expression recognition in image sequences using geometric deformation features and support vector machines. IEEE Transactions on Image Processing, 2007, 16(1): 172–187 https://doi.org/10.1109/TIP.2006.884954
13
Sebe N, Lew M S, Cohen I, Sun Y, Gevers T, Huang T S. Authentic facial expression analysis. Image and Vision Computing, 2007, 25(12): 1856–1863 https://doi.org/10.1016/j.imavis.2005.12.021
14
Candès E J, Romberg J, Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 2006, 52(2): 489–509 https://doi.org/10.1109/TIT.2005.862083
Mallat S G, Zhang Z. Matching pursuits with time-frequency dictionaries. IEEE Transactions on Signal Processing, 1993, 41(12): 3397–3415 https://doi.org/10.1109/78.258082
17
Wright J, Yang A Y, Ganesh A, Sastry S, Ma Y. Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2): 210–227 https://doi.org/10.1109/TPAMI.2008.79
18
Yang M, Zhang L. Gabor feature based sparse representation for face recognition with gabor occlusion dictionary. In: Proceedings of European Conference on Computer Vision. 2010 https://doi.org/10.1007/978-3-642-15567-3_33
19
Zheng H, Xie J C, Jin Z. Heteroscedastic sparse representation classification for face recognition. Neural Processing Letters, 2012, 35(3): 233–244 https://doi.org/10.1007/s11063-012-9214-4
20
Yuan M, Lin Y. Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2006, 68(1): 49–67 https://doi.org/10.1111/j.1467-9868.2005.00532.x
21
Hinton G, Salakhutdinov R. Reducing the dimensionality of data with neural networks. Science, 2006, 313(5786): 504–507 https://doi.org/10.1126/science.1127647
22
Lai Z H, Wong W K, Xu Y, Yang J, Tang J, Zhang D. Approximate orthogonal sparse embedding for dimensionality reduction. IEEE Transactions on Neural Networks and Learning Systems, 2016, 27(4): 723–735 https://doi.org/10.1109/TNNLS.2015.2422994
23
Lai Z H, Xu Y, Chen Q C, Yang J, Zhang D. Multilinear sparse principal component analysis. IEEE Transactions on Neural Networks and Learning Systems, 2014, 25(10): 1942–1950 https://doi.org/10.1109/TNNLS.2013.2297381
24
Lai Z H, Wong W H, Xu Y. Sparse alignment for robust tensor learning. IEEE Transactions on Neural Networks and Learning Systems, 2014, 25(10): 1779–1792 https://doi.org/10.1109/TNNLS.2013.2295717
25
Yuan M, Lin Y. Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2006, 68(1): 49–67 https://doi.org/10.1111/j.1467-9868.2005.00532.x
26
Huang J Z, Zhang T. The benefit of group sparsity. The Annals of Statistics, 2010, 38(4): 1978–2004 https://doi.org/10.1214/09-AOS778
27
Yuan X T, Liu X B, Yan S C. Visual classification with multitask joint sparse representation. IEEE Transactions on Image Processing, 2012, 21(10): 4349–4360 https://doi.org/10.1109/TIP.2012.2205006
28
Lin Y Y, Liu T L, Fuh C S. Local ensemble kernel learning for object category recognition. In: Proceedings of IEEE Conference on Computer Vision (ICCV). 2007 https://doi.org/10.1109/cvpr.2007.383084
29
Negahban S, Wainwright M J. Estimation of (near) low-rank matrices with noise and high-dimensional scaling. The Annals of Statistics, 2011, 39(2): 1069–1097 https://doi.org/10.1214/10-AOS850
30
Pong T K, Tseng P, Ji S, Ye J. Trace norm regularization: reformulations, algorithms, and multi-task learning. SIAM Journal on Optimization, 2010, 20(6): 3465–3489 https://doi.org/10.1137/090763184
31
He Z F, Yang M, Liu H D. Multi-task joint feature selection for multilabel classification. Chinese Journal of Electronics, 2015, 24(2): 281–287 https://doi.org/10.1049/cje.2015.04.009
32
Cheng X, Li N J, Zhou T C, Wu Z Y, Zhou L. Multi-task object tracking with feature selection. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, 2015, (6): 1351–1354 https://doi.org/10.1587/transfun.E98.A.1351
33
Zheng H, Geng X, Tao D C, Jin Z. A multi-task model for simultaneous face identification and facial expression recognition. Neurocomputing, 2016, 171(1): 515–523 https://doi.org/10.1016/j.neucom.2015.06.079
34
Bakker B, Heskes T. Task clustering and gating for bayesian multitask learning. Journal of Machine Learning Research, 2003, 4(4): 83–99
35
Evgeniou T, Pontil M. Regularized multi-task learning. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2004 https://doi.org/10.1145/1014052.1014067
36
Ando R K, Zhang T. A framework for learning predictive from multiple tasks and unlabeled data. Journal of Machine Learning Research, 2005, 6: 1817–1853
37
Parameswaran S, Weinberger K Q. Large margin multi-task metric learning. In: Proceedings of Advances in Neural Information Processing Systems. 2010, 1867–1875
38
Zhang Y, Yeung D Y. Transfer metric learning by learning task relationships. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2010 https://doi.org/10.1145/1835804.1835954
39
Chen X, Pan W K, Kwok J T, Garbonell J G. Accelerated gradient method for multi-task sparse learning problem. In: Proceedings of IEEE International Conference on Data Mining. 2009 https://doi.org/10.1109/icdm.2009.128
40
Gehler P, Nowozin S. On feature combination for multiclass object classification. In: Proceedings of the 12th IEEE International Conference on Computer Vision. 2009, 221–228 https://doi.org/10.1109/iccv.2009.5459169
41
Kanade T, Cohn J F, Tian Y. Comprehensive database for facial expression analysis. In: Proceedings of the 4th IEEE International Conference on Automatic Face and Gesture Recognition. 2000, 46–53 https://doi.org/10.1109/afgr.2000.840611
42
Wang J, Yin L J, Wei X Z, Sun Y. 3D facial expression recognition based on primitive surface feature distribution. In: Proceedings of IEEE Society Conference on Computer Vision and Pattern Recognition. 2006, 1399–1406
43
Dhall A, Goecke R, joshi J, Wagner M, Gedeon T. Emotion recognitionin the wild challenge 2013. In: Proceedings of the 15th ACM International Conference on Multimodal Interaction. 2013, 509–516 https://doi.org/10.1145/2522848.2531739
