Robust Multipose Face Detection in Images

IEEE Transactions on Circuits and Systems for Video Technology

Volumn 14, Issue 1, 2004, Pages 31-41

  Xiao, Rong ^a   Li, Ming Jing ^a   Zhang, Hong Jiang ^a  

^a MICROSOFT RESEARCH ASIA   (China)

Author keywords

AdaBoost; Face detection; Support vector machine (SVM)

Indexed keywords

ALGORITHMS; COMPUTATIONAL METHODS; DECISION MAKING; FUNCTIONS; HIERARCHICAL SYSTEMS; OPTICAL FILTERS; VECTORS;

BIOMETRICS; SUPPORT VECTOR MACHINES (SVM);

FACE RECOGNITION;

EID: 0742325003     PISSN: 10518215     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCSVT.2003.818351     Document Type: Article

References (19)

1. A. Pentland, B. Moghaddam, and T. Starner, "View-based and modular eigenspaces of face recognition," in Proc. IEEE Computer Soc. Conf. on Computer Vision and Pattern Recognition, Seattle, WA, June 1994, pp. 84-91.
2. C. P. Papageorgiou, M. Oren, and T. Poggio, "A general framework for object detection," in Proc. 6th Int. Conf. Computer Vision, Jan. 1998, pp. 555-562.
3. D. Roth, M. Yang, and N. Ahuja, "A SNoW-based face detection," in Advances in Neural Information Processing Systems, S. A. Solla, T. K. Leen, and K.-R. Muller, Eds. Cambridge, MA: MIT Press, 2000, vol. 12, pp. 855-861.
4. E. Osuna, R. Freund, and F. Girosi, "Training support vector machines: An application to face detection," in Proc. IEEE Computer Soc. Conf. on Computer Vision and Pattern Recognition, June 1997, pp. 130-136.
5. F. Fleuret and D. Geman, "Coarse-to-fine face detection," Int. J. Computer Vision, vol. 20, pp. 1157-1163, 2001.
6. H. Schneiderman and T. Kanade, "A statistical method for 3D object detection applied to faces and cars," in Proc. IEEE Computer Soc. Conf. on Computer Vision and Pattern Recognition, June 2000, pp. 746-751.
7. H. A. Rowley, S. Baluja, and T. Kanade, "Neural network-based face detection," IEEE Trans. Pattern Anal. Machine Intell., vol. 20, pp. 22-38, 1998.
8. Neural Network-Based Face Detection, H. A. Rowley.http://www-2.cs.cmu.edu/̃har/thesis.ps.gz [Online]
9. J. Ng and S. Gong, "Performing multi-view face detection and pose estimation using a composite support vector machine across the view sphere," in Proc. IEEE Int. Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, Corfu, Greece, Sept. 1999, pp. 14-21.
10. M. Alvira and R. Rifkin, "An Empirical Comparison of SNoW and SVM's for Face Detection," Massachusetts Inst. of Technology, Cambridge, MA, CBCL Paper #193/AI Memo #2001-004, 2001.
11. M. Bichsel and A. P. Pentland, "Human face recognition and the face image set's topology," CVGIP: Image Understanding, vol. 59, pp. 254-261, 1994.
12. P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features," in Proc. 2001 IEEE Computer Soc. Computer Vision and Pattern Recognition, vol. 1, HI, Dec 2001, pp. 511-518.
13. R. E. Schapire. The boosting approach to machine learning: An overview. presented at Proc. MSRI Workshop on Nonlinear Estimation and Classification [Online]
14. R. L. Hsu, M. Abdel-Mottaleb, and A. K. Jain, "Face detection in color images," IEEE Trans. Pattern Anal. Machine Intell., vol. 24, pp. 696-706, May 2002.
15. S. Z. Li et al., "Statistical learning of multi-view face detection," in Proc. 7th Eur. Conf. Computer Vision., Copenhagen, Denmark, May 2002, pp. 67-81.
16. T. Poggio and K. K. Sung, "Example-based learning for view-based human face detection," in Proc. ARPA Image Understanding Workshop, vol. II, 1994, pp. 843-850.
17. T. Serre et al., "Feature selection for face detection," in AI Memo 1697: Massachusetts Institute of Technology, 2000.
18. V. N. Vapnik, Statistical Learning Theory. New York: Wiley, 1998.
19. Y. Freund and R. E. Schapire, "A decision-theoretic generalization of on-line learning and an application to boosting," J. Computer Syst. Sci., vol. 55, no. 1, pp. 119-139, Aug. 1997.