Manifold based sparse representation for robust expression recognition without neutral subtraction

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2011, Pages 2136-2143

  Ptucha, Raymond ^a   Tsagkatakis, Grigorios ^a   Savakis, Andreas ^a  

^a ROCHESTER INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA SETS; EXPERIMENTAL PROCEDURE; EXPRESSION RECOGNITION; FACIAL EXPRESSION RECOGNITION; FACIAL EXPRESSIONS; FACIAL FEATURE; FACIAL REGIONS; LOCALITY PRESERVING PROJECTIONS; LOW-DIMENSIONAL MANIFOLDS; MANIFOLD LEARNING; MIXTURE MODEL; RECONSTRUCTION ERROR; SPARSE METHODS; SPARSE REPRESENTATION; SPARSE SIGNAL REPRESENTATION;

GESTURE RECOGNITION;

FACE RECOGNITION;

EID: 84856642761     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCVW.2011.6130512     Document Type: Conference Paper

References (27)

1. L. Shuai-Shi, T. Yan-Tao, and L. Dong, "New research advances of facial expression recognition, " Eighth International Conference on Machine Learning and Cybernetics, Piscataway, NJ, USA, 2009.
2. B. Fasel and J. Luettin, "Automatic facial expression analysis: A survey, " Pattern Recognition, vol. 36, pp. 259-275, 2003.
3. T. F. Cootes, G. J. Edwards, and C. J. Taylor, "Active appearance models, " IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, pp. 681-5, 2001.
4. C. Yeongjae and K. Daijin, "Natural facial expression recognition using differential-AAM and manifold learning, " Pattern Recognition, vol. 42, pp. 1340-50, 2009.
5. C. Martin, U. Werner, and H. M. Gross, "A real-time facial expression recognition system based on active appearance models using gray images and edge images, " in IEEE Conf. on Automatic Face & Gesture Recognition, 2008.
6. R. Ptucha and A. Savakis, "Facial Expression Recognition Using Facial Features and Manifold Learning, " in International Symposium on Visual Computing, NV, 2010.
7. I. Buciu, C. kotropoulos, and I. Pitas, "ICA and Gabor representation for facial expression recognition, " International Conference on Image Processing, 2003.
8. X. Feng, M. Pietikainen, and A. Hadid, "Facial expression recognition based on local binary patterns, " Pattern Recognition and Image Analysis, vol. 17, 2007.
9. C. Shan, S. Gong, and P. W. McOwan, "Facial expression recognition based on Local Binary Patterns: A comprehensive study, " Image and Vision Computing, vol. 27, pp. 803-816, 2009.
10. B. Jiang, M. F. Valstar, and M. Pantic, "Action Unit Detection Using Sparse Appearance Descriptors in Space-Time Video Volumes, " in Face and Gesture Recognition, Santa Barbara, CA, 2011.
11. N. Kumar, P. Belhumeur, and S. Nayar, "FaceTracer: a search engine for large collections of images with faces, " in Computer Vision. 10th European Conference on Computer Vision, ECCV 2008, Berlin, Germany, 2008.
12. A. Ghodsi, "Dimensionality Reduction A Short Tutorial, " University of Waterloo, Ontario, Canada, 2006.
13. L. Cayton, "Algorithms for manifold learning, " University of California, San Diego, Tech Rep. CS2008-0923, 2005.
14. X. He and P. Niyogi, "Locality Preserving Projections, " in Advances in Neural Information Processing Systems 16, Vancouver, Canada, 2003.
15. C. Shan, S. Gong, and P. W. McOwan, "Appearance manifold of facial expression, " in Computer Vision in Human-Computer Interaction. ICCV 2005 Workshop on HCI. 5, Berlin, Germany, 2005.
16. J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and M. Yi, "Robust face recognition via sparse representation, " IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, pp. 210-27, 2009.
17. S. Zafeiriou and M. Petrou, "Sparse representations for facial expressions recognition via l1 optimization, " in IEEE Conference on Computer Vision and Pattern Recognition, 2010.
18. P. Lucey, J. F. Cohn, T. Kanade, J. Saragih, Z. Ambadar, and I. Matthews, "The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotionspecified expression, " in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010.
19. T. Banziger and K. R. Scherer, "Introducing the Geneva Multimodal Emotion Portrayal (GEMEP) Corpus, " in Blueprint for Affective Computing: A Sourcebook, ed Oxford: Oxford University Press, 2010, pp. 271-294.
20. M. Valstar, B. Jiang, M. Mehu, M. Pantic, and K. R. Scherer, "The First Facial Expression Recognition and Analysis Challenge, " in Face and Gesture Recognition, Santa Barbara, CA, 2011.
21. X. Rui, Z. Qijun, D. Zhang, and S. Pengfei, "Facial expression recognition on multiple manifolds, " Pattern Recognition, vol. 44, pp. 107-16, 2011.
22. D. L. Donoho, M. Elad, and V. N. Temlyakov, "Stable recovery of sparse overcomplete representations in the presence of noise, " IEEE Transactions on Information Theory, vol. 52, pp. 6-18, 2006.
23. E. J. Candes, J. Romberg, and T. Tao, "Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information, " IEEE Transactions on Information Theory, vol. 52, pp. 489-509, 2006.
24. T. Kanade, J. F. Cohn, and T. Yingli, "Comprehensive database for facial expression analysis, " in Proceedings of the Fourth International Conference on Automatic Face and Gesture Recognition, Los Alamitos, CA, USA, 2000.
25. Z. Ruicong and R. Qiuqi, "Discriminant sparse nonnegative matrix factorization, " in IEEE International Conference on Multimedia and Expo (ICME), 2009.
26. S. Chew, P. Lucey, S. Lucey, J. Saragih, J. Cohn, and S. Sridharan, "Person-Independent Facial Expression Detection Using Constrained Local Models, " in Automatic Face and Gesture Recognition, Santa Barbara, CA, 2011.
27. R. Rigamonti, M. Brown, and V. Lepetit, "Are Sparse Representations Really Relevant for Image Classification?, " in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Colorado Springs, Co., 2011.