Fast and robust face detection using evolutionary pruning

IEEE Transactions on Evolutionary Computation

Volumn 12, Issue 5, 2008, Pages 562-571

  Jang, Jun Su ^a   Kim, Jong Hwan ^a  

^a Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

Author keywords

AdaBoost learning; Constrained optimization; Evolutionary computer vision; Face detection; Pattern recognition

Indexed keywords

DETECTORS; EVOLUTIONARY ALGORITHMS; FACE RECOGNITION; LEARNING SYSTEMS; MATHEMATICAL MODELS;

ADABOOST LEARNING; CONSTRAINED OPTIMIZATION; EVOLUTIONARY COMPUTER VISION; FACE DETECTION; PATTERN RECOGNITION;

CLASSIFIERS;

EID: 53349178742     PISSN: 1089778X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TEVC.2007.910140     Document Type: Article

References (25)

1. M.-H. Yang, D. Kriegman, and N. Ahuja, "Detecting faces in images: A survey," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 1, pp. 34-58, 2002.
2. P. Viola and M. Jones, "Robust real-time face detection," Int. J. Computer Vision, vol. 57, no. 2, pp. 137-154, 2004.
3. S. Z. Li and Z. Q. Zhang, "Floatboost learning and statistical face detection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 9, pp. 1112-1123, Sep. 2004.
4. B. Wu, H. Ai, C. Huang, and S. Lao, "Fast rotation invariant multi-view face detection based on real Adaboost," in Proc. 6th Int. Conf. Autom. Face Gesture Recogn., 2004, pp. 79-84.
5. J.-S. Jang, K.-H. Han, and J.-H. Kim, "Evolutionary algorithm-based face verification," Pattern Recogn. Lett., vol. 25, pp. 1857-1865, 2004.
6. J.-S. Jang and J.-H. Kim, "Evolutionary pruning for fast and robust face detection," in Proc. IEEE Congr. Evol. Comput., 2006, pp. 1293-1299.
7. A. Treptow and A. Zell, "Combining Adaboost learning and evolutionary search to select features for real-time object detection," in Proc. IEEE Congr. Evol. Comput., 2004, pp. 2107-2113.
8. C. Liu and H. Wechsler, "Evolutionary pursuit and its application to face recognition," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 6, pp. 570-582, 2000.
9. Y. Abramson, F. Moutarde, B. Stanciulescu, and B. Steux, "Combining adaboost with a hill-climbing evolutionary feature search for efficient training of performant visual object detectors," in Proc. 7th Int. FLINS Conf. Appl. Artif. Intell., 2006, pp. 737-744.
10. Y. Freund and R. E. Schapire, "A decision-theoretic generalization of on-line learning and an application to boosting," J. Comput. Syst. Sci., vol. 55, no. 1, pp. 119-139, 1997.
11. X. Wang and H. Wang, "Classification by evolutionary ensembles," Pattern Recogn., vol. 39, pp. 595-607, 2006.
12. Z.-H. Zhou, J. Wu, and W. Tang, "Ensembling neural networks: Many could be better than all," Artif. Intell., vol. 137, no. 1-2, pp. 239-263, 2002.
13. R. Xiao, M.-J. Li, and H.-J. Zhang, "Robust multipose face detection in images," IEEE Trans. Circuits Syst. Video Technol., vol. 14, no. 1, pp. 31-41, Jan. 2004.
14. F. Crow, "Summed-area tables for texture mapping," in Proc. SIGGRAPH, 1984, vol. 18, pp. 207-212.
15. H. A. Rowley, S. Baluja, and T. Kanade, "Neural network-based face detection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 1, pp. 23-38, 1998.
16. B. Heisele, T. Serre, S. Mukherjee, and T. Poggio, "Feature reduction and hierarchy of classifiers for fast object detection in video images," in Proc. IEEE Conf. Comput. Vision Pattern Recogn., 2001, pp. 18-24.
17. J. Šochman and J. Matas, "Inter-stage feature propagation in cascade building with AdaBoost," in Proc. Int. Conf. Pattern Recogn., 2004, pp. 236-239.
18. T. Bäck, U. Hammel, and H.-P. Schwefel, "Evolutionary computation: Comments on the history and current state," IEEE Trans. Evol. Comput., vol. 1, no. 1, pp. 3-17, Apr. 1997.
19. H.-P. Schwefel, Evolution and Optimum Seeking. New York: Wiley, 1995.
20. T. Bäck, Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. New York: Oxford Univ. Press, 1996.
21. J. Wu, J. M. Rehg, and M. D. Mullin, "Learning a rare event detection cascade by direct feature selection," Advances in Neural Information Processing Systems, vol. 16, 2003.
22. J. Wu, M. D. Mullin, and J. M. Rehg, "Linear asymmetric classifier for face detectors," in Proc. Int. Conf. Mach. Learn., 2005, pp. 988-995.
23. H. A. Rowley, "Neural network-based face detection," Ph.D., Carnegie Mellon Univ., Pittsburgh, PA, 1999.
24. H. Schneiderman and T. Kanade, "A statistical method for 3D object detection applied to faces and cars," in Proc. IEEE Int. Conf. Comput. Vision, Pattern Recogn., 2000, vol. 1, pp. 746-751.
25. P. Viola and M. Jones, Fast multi-view face detection Mitsubishi Electric Research Laboratories, Tech. Rep., TR2003-96, 2003.