A deep sum-product architecture for robust facial attributes analysis

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2013, Pages 2864-2871

  Luo, Ping ^a,b   Wang, Xiaogang ^a   Tang, Xiaoou ^a,b  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

^b SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY   (China)

Author keywords

attributes; deep learning; face recognition

Indexed keywords

ALGORITHMS; DECISION TREES;

ATTRIBUTES; DEEP LEARNING; EM ALGORITHMS; FACE REGIONS; HIGH ORDER CORRELATION; LARGE DATASETS; LOCAL REGION; MOST PROBABLE EXPLANATION;

FACE RECOGNITION;

EID: 84898796864     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2013.356     Document Type: Conference Paper

References (26)

1. A. Bosch, A. Zisserman, and X. Munoz. Image classification using random forests and ferns. ICCV, 2007.
2. L. Bourdeva, S. Maji, and J. Malik. Describing people: Poselet-based approach to attribute classification. ICCV, 2011.
3. Z. Cao, Q. Yin, X. Tang, and J. Sun. Face recognition with learning-based descriptor. CVPR, 2010.
4. M. Chen, Z. Xu, K. Weinberger, and F. Sha. Marginalized denoising autoencoders for domain adaptation. ICML, 2012.
5. A. Darwiche. A differential approach to inference in bayesian networks. Journal of the ACM, 2003.
6. P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained part based models. PAMI, 2010.
7. R. Gens and P. Domingos. Discriminative learning of sumproduct networks. NIPS, 2012.
8. G. E. Hinton and R. R. Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 2006.
9. G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller. Labeled faces in the wild: A database for studying face recognition in unconstrained environments. University of Massachusetts, Amherst, Technical Report 07-49, 2007.
10. F. V. Jensena, S. L. Lauritzen, and K. G. Olesen. Bayesian updating in causal probabilistic networks by local computations. Computational Statistics Quarterly, 1990.
11. N. Kumar, P. N. Belhumeur, and S. K. Nayar. Facetracer: A search engine for large collections of images with faces. ECCV, pages 340-353, 2008.
12. N. Kumar, A. C. Berg, P. N. Belhumeur, and S. K. Nayar. Attribute and simle classifers for face verification. ICCV, 2009.
13. Y. LeCun, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998.
14. P. Luo, X. Wang, and X. Tang. Hierachical face parsing via deep learning. CVPR, 2012.
15. P. Luo, X. Wang, and X. Tang. Pedestrian parsing via deep decompositional network. ICCV, 2013.
16. W. Ouyang and X. Wang. A discriminative deep model for pedestrian detection with occlusion handling. CVPR, 2012.
17. H. Poon and P. Domingos. Sum-product networks: A new deep architecture. UAI, 2011.
18. N. L. Roux, N. Heess, J. Shotton, and J. Winn. Learning a generative model of images by factoring appearance and shape. Neural Computation, 2011.
19. B. Siddiquie, R. S. Feris, and L. S. Davis. Image ranking and retrieval based on multi-attribute queries. CVPR, 2011.
20. Y. Tang. Gated boltzmann machine for recognition under occlusion. NIPS Workshop on Transfer Learning by Learning Rich Generative Models, 2010.
21. Y. Tang, R. Salakhutdinov, and G. Hinton. Robust boltzmann machines for recognition and denoising. CVPR, 2012.
22. P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. JMLR, 2010.
23. X. Wang and X. Tang. A unified framework for subspace face recognition. TPAMI, 2004.
24. X. Wang and X. Tang. Random sampling for subspace face recognition. IJCV, 2006.
25. B. Yao, A. Khosla, and L. Fei-Fei. Combining randomization and discrimination for fine-grained image categorization. CVPR, 2011.
26. Q. Yin, X. Tang, and J. Sun. An associate-predict model for face recognition. CVPR, 2011.