Web image mining towards universal age estimator

MM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums

Volumn , Issue , 2009, Pages 85-94

  Ni, Bingbing ^a   Song, Zheng ^a   Yan, Shuicheng ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Age estimation; Internet vision; Multi instance regression

Indexed keywords

AGE ESTIMATION; AGE-RELATED; BENCHMARK DATABASE; ETHNIC GROUPS; FACE DETECTION; GENERALIZATION CAPABILITY; HUMAN AGING; HUMAN FACES; IMAGE SEARCH ENGINE; IMAGE SETS; INTERNET VISION; LEARNING PROCESS; MULTIPLE INSTANCES; MULTIPLE RESPONSE; TEXT QUERY; TRAINING SAMPLE; WEB IMAGES;

FLUORINE CONTAINING POLYMERS; IMAGE QUALITY; IMAGE RETRIEVAL; INTERNET; LEARNING ALGORITHMS; METHOD OF MOMENTS; MULTIMEDIA SYSTEMS; PRINCIPAL COMPONENT ANALYSIS; REGRESSION ANALYSIS; SEARCH ENGINES; TECHNICAL PRESENTATIONS;

FACE RECOGNITION;

EID: 72449166954     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1631272.1631287     Document Type: Conference Paper

References (25)

1. The fg-net aging database: http://sting.cycollege.ac.cy/ alanitis/fgnetaging.html.
2. S. Andrews, I. Tsochantaridis, and T. Hofmann. Support vector machines for multiple-instance learning. In Neural Information Processing Systems, 2002.
3. Y. Chen, J. Bi, and J. Wang. Multiple-instance learning via embedded instance selection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12):1931 lC1947, 2006.
4. T. Cootes, G. Edwards, and C. Taylor. Active appearance models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(6):681-685, 2001.
5. A. Dempster, N. Laird, and D. Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society, 39(1):1-38, 1977.
6. Y. Fu and T. Huang. Human age estimation with regression on discriminative aging manifold. IEEE Transactions on Multimedia, 10(4):578-584, 2008.
7. X. Geng, Z. Zhou, and K. Smith-Miles. Automatic age estimation based on facial aging patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(12):2234-2240, 2007.
8. G. Guo, Y. Fu, C. Dyer, and T. Huang. Image-based human age estimation by manifold learning and locally adjusted robust regression. IEEE Transactions on Image Processing, 17(7):1178-1188, 2008.
9. J. Hayashi, M. Yasumoto, H. Ito, and H. Koshimizu. A method for estimating and modeling age and gender using facial image processing. In International Conference on Virtual Systems and Multimedia, pages 439-448, 2001.
10. I. Joliffe. Principal component analysis. Springer-Verlag, New York, 1986.
11. J. Keeler, D. Rumelhart, and W. Leow. Integrated segmentation and recognition of hand-printed numerals. In Neural Information Processing Systems, pages 557-563, 1990.
12. Y. Kwon and N. Lobo. Age classification from facial images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 74(1):1-21, 1999.
13. A. Lanitis, C. Draganova, and C. Christodoulou. Comparing different classifiers for automatic age estimation. IEEE Transactions on Systems, Man and Cybernetics, Part B, 34(1):621-628, 2004.
14. O. Maron and T. Lozano-Płȩrez. A framework for multiple-instance learning. In Neural Information Processing Systems, pages 570-576, 1998.
15. S. Ray and D. Page. Multiple instance regression. In International Conference on Machine Learning, pages 425-432, 2001.
16. K. Ricanek and T. Tesafaye. Morph: A longitudinal image database of normal adult age-progression. In IEEE International Conference on Automatic Face and Gesture Recognition, pages 341-345, March 2006.
17. W. Rudin. Principles of Mathematical Analysis, 3nd Edition. McGray-Hill, 1978.
18. P. Viola and M. Jones. Robust real-time face detection. In International Conference on Computer Vision, 2001.
19. P. Viola, J. Platt, and C. Zhang. Multiple instance boosting for object detection. In Neural Information Processing Systems, 2005.
20. J. Wang and J. Zucker. Solving the multiple-instance problem: a lazy learning approach. In International Conference on Machine Learning, pages 1119-1125, 2000.
21. S. Yan, H. Wang, X. Tang, J. Liu, and T. Huang. Regression from uncertain labels and its applications to soft-biometrics. IEEE Transactions on Information Forensics and Security, 3(4):698-708.
22. S. Yan, X. Zhou, M. Liu, M. Hasegawa-Johnson, and T. S. Huang. Regression from patch-kernel. In IEEE Conference on Computer Vision and Pattern Recognition, pages 1-8, 2008.
23. K. Yanai and K. Barnard. Finding visual concept by web image mining. In International World Wide Web Conference, 2006.
24. Q. Zhang and S. Goldman. Em-dd: An improved multiple-instance learning technique. In Neural Information Processing Systems, 2001.
25. Z. Zhou and M. Zhang. Multi-instance multi-label learning with application to scene classification. In Neural Information Processing Systems, 2007.