© The Institution of Engineering and Technology
In this study, an unsupervised feature selection method is proposed for facial feature recognition (FER) in the absence of class labels. The contribution is the descriptive feature components selector spectral regression representative coefficient scores based on graph manifold learning from high-dimensional feature space. The spectral regression analysis and L1-regularised least square are then used to compute the importance of features in the original space, so that less representative features with lower coefficient scores will be removed without prior distribution assumption. To verify the performance of the authors’ method, some classifiers are used to classify facial expressions on three benchmark facial expression databases. The recognition results indicate the availability and effectiveness of the proposed method for FER.
References
-
-
1)
-
28. Belhumeur, P., Hespanha, J., Kriegman, D.: ‘Eigenfaces vs. Fisherfaces: recognition using class specific linear projection’, IEEE Trans. Pattern Anal. Mach. Intell., 1997, 19, pp. 711–720 (doi: 10.1109/34.598228).
-
2)
-
10. Elguebaly, T., Bouguila, N.: ‘Simultaneous high-dimensional clustering and feature selection using asymmetric Gaussian mixture models’, Image Vis. Comput., 2015, 34, pp. 27–41 (doi: 10.1016/j.imavis.2014.10.011).
-
3)
-
13. Rudovic, O., Pavlovic, V., Pantic, M.: ‘Multi-output Laplacian dynamic ordinal regression for facial expression recognition and intensity estimation’. CVPR, 2012.
-
4)
-
16. Nikitidis, S., Tefas, A., Pitas, I.: ‘Maximum margin projection subspace learning for visual data analysis’, IEEE Trans. Image Process., 2014, 23, (10), pp. 4413–4425 (doi: 10.1109/TIP.2014.2348868).
-
5)
-
20. Cai, D., Zhang, C., He, X.: ‘Unsupervised feature selection for multi-cluster data’. Sixteenth ACM SIGKDD Conf. on Knowledge Discovery and Data Mining, July 2010, pp. 333–342.
-
6)
-
19. Cai, D., He, X., Han, J.: ‘SRDA: an efficient algorithm for large-scale discriminant analysis’, IEEE Trans. Knowl. Data Eng., 2008, 20, (1), pp. 1–12 (doi: 10.1109/TKDE.2007.190669).
-
7)
-
27. Pantic, M., Valstar, M., Rademaker, R., et al: ‘Web-based database for facial expression analysis’. Proc. of IEEE Int. Conf. on Multimedia and Expo (ICME'05), Amsterdam, Netherlands, July 2005, pp. 317–321.
-
8)
-
10. Cootes, T., Taylor, C., Cooper, D., Graham, J.: ‘Active shape models – their training and application’, Comput. Vis. Image Underst., 1995, 61, (1), pp. 38–59 (doi: 10.1006/cviu.1995.1004).
-
9)
-
8. Liu, P., Zhou, T., Tsang, I., Meng, Z., Han, S., Tong, Y.: ‘Feature disentangling machine – a novel approach of feature selection and disentangling in facial expression analysis’. ECCV, 2014.
-
10)
-
3. Jain, A.K., Duin, R.P.W., Mao, J.: ‘Statistical pattern recognition: a review’, IEEE Trans. Pattern Anal. Mach. Intell., 2000, 22, (1), pp. 4–37 (doi: 10.1109/34.824819).
-
11)
-
1. Yang, J., Zhang, D., Yang, J., Niu, B.: ‘Globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics’, IEEE Trans. Pattern Anal. Mach. Intell., 2007, 29, (4), pp. 650–664 (doi: 10.1109/TPAMI.2007.1008).
-
12)
-
22. Chang, C.-C., Lin, C.-J.: ‘LIBSVM: a library for support vector machines’, , 2001.
-
13)
-
28. Kanade, T., Cohn, J.F., Tian, Y.: ‘Comprehensive database for facial expression analysis’. Proc. of the Fourth Int. Conf. on Face and Gesture Recognition, Grenoble, France, March 2000, pp. 46–53.
-
14)
-
15. Papachristou, K., Tefas, A., Pitas, I.: ‘Symmetric subspace learning for image analysis’, IEEE Trans. Image Process., 2014, 23, (2), pp. 5683–5697 (doi: 10.1109/TIP.2014.2367321).
-
15)
-
4. Valstar, M., Mehu, M., Jiang, B., Pantic, M., Klaus, S.: ‘Meta-analysis of the first facial expression recognition challenge’, IEEE Trans. Syst. Man Cybern. B, 2012, 42, (4), pp. 966–979 (doi: 10.1109/TSMCB.2012.2200675).
-
16)
-
30. Wang, K., Li, R., Zhao, L.: ‘Real-time facial expression recognition system for service robot based-on ASM and SVMs’. Eighth World Congress on Intelligent Control and Automation, 2010, Jinan, China, July 2010, pp. 6637–6641.
-
17)
-
26. Lyons, M., Akamatsu, S., Kamachi, M., et al: ‘Coding facial expressions with Gabor wavelets’. .
-
18)
-
32. Ashraf, A.B., Lucey, S., Chen, T.: ‘Reinterpreting the application of Gabor filters as a manipulation of the margin in linear support vector machines’, IEEE Trans. Pattern Anal. Mach. Intell., 2010, 32, (7), pp. 1335–1341 (doi: 10.1109/TPAMI.2010.75).
-
19)
-
7. Happy, S.L., Routray, A.: ‘Automatic facial expression recognition using features of salient facial patches’, IEEE Trans. Affective Comput., 2015, 6, (1), pp. 1–12 (doi: 10.1109/TAFFC.2014.2386334).
-
20)
-
25. Duda, R.O., Hart, P.E., Stork, D.G.: ‘Pattern classification’ (Wiley, 2000, 2nd edn.).
-
21)
-
18. He, X., Niyogi, P.: ‘Locality preserving projections’. Proc. of 17th Annual Conf. on Neural Information Processing System, Vancouver and Whistler, Canada, December 2005, pp. 8–13.
-
22)
-
24. He, X., Cai, D., Yan, S., Zhang, H.-J.: ‘Neighborhood preserving embedding’. Proc. of IEEE Int. Conf. on Computer Vision, October 2005, pp. 1208–1213.
-
23)
-
9. Liu, P., Han, S., Meng, Z., Tong, Y.: ‘Facial expression recognition via a boosted deep belief network’. CVPR, 2014.
-
24)
-
3. Wang, R., Shan, S., Chen, X., Chen, J., Gao, W.: ‘Maximal linear embedding for dimensionality reduction’, IEEE Trans. Pattern Anal. Mach. Intell., 2011, 33, (9), pp. 1776–1792 (doi: 10.1109/TPAMI.2011.39).
-
25)
-
11. Liu, M., Shan, S., Wang, R., Chen, X.: ‘Learning expression lets on spatio-temporal manifold for dynamic facial expression recognition’. CVPR, 2014.
-
26)
-
5. Liu, M., Li, S., Shan, S., Chen, X.: ‘AU-aware deep network for facial expression recognition’. Tenth IEEE Int. Conf. and Workshops on Automatic Face and Gesture Recognition (FG), 2013, 2013, pp. 1–6.
-
27)
-
6. Zhong, L., Liu, Q., Yang, P., Liu, B., Huang, J., Metaxas, D.: ‘Learning active facial patches for expression analysis’. CVPR, 2012.
-
28)
-
1. Pan, S.J., Yang, Q.: ‘A survey on transfer learning’, IEEE Trans. Knowl. Data Eng., 2010, 22, pp. 1345–1359 (doi: 10.1109/TKDE.2009.191).
-
29)
-
12. Yi, J., Mao, X., Chen, L., Xue, Y., Compare, A.: ‘Facial expression recognition considering individual differences in facial structure and texture’, IET Comput. Vis., 2013, 8, (5), pp. 429–440 (doi: 10.1049/iet-cvi.2013.0171).
-
30)
-
11. Guoying, Z., Pietikainen, M.: ‘Dynamic texture recognition using local binary patterns with an application to facial expressions’, IEEE Trans. Pattern Anal. Mach. Intell., 2007, 29, (6), pp. 915–928 (doi: 10.1109/TPAMI.2007.1110).
-
31)
-
14. Lee, S.H., Plataniotis, K.N., Ro, Y.M.: ‘Intra-class variation reduction using training expression images for sparse representation based facial expression recognition’, IEEE Trans. Affective Comput., 2014, 5, (3), pp. 340–531 (doi: 10.1109/TAFFC.2014.2346515).
-
32)
-
21. Kwak, N.: ‘Principle component analysis based on L1-norm maximization’, IEEE Trans. Pattern Anal. Mach. Intell., 2008, 30, (9), pp. 1672–1680 (doi: 10.1109/TPAMI.2008.114).
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2014.0278
Related content
content/journals/10.1049/iet-cvi.2014.0278
pub_keyword,iet_inspecKeyword,pub_concept
6
6