A new biologically inspired active appearance model for face age estimation by using local ordinal ranking

ACM International Conference Proceeding Series

Volumn , Issue , 2013, Pages 327-330

  Hong, Lijun ^a   Wen, Di ^a   Fang, Chi ^a   Ding, Xiaoqing ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

active appearance model; biologically inspired feature; face age estimation; machine learning

Indexed keywords

ACTIVE APPEARANCE MODELS; AGE ESTIMATION; BIOLOGICALLY INSPIRED; EXPERIMENTAL EVALUATION; MEAN ABSOLUTE ERROR; MULTIPLE LINEAR REGRESSIONS; ORDINAL RANKING; STATE-OF-THE-ART ALGORITHMS;

IMAGE RECOGNITION; INTERNET; LEARNING ALGORITHMS; LEARNING SYSTEMS; LINEAR REGRESSION; QUERY PROCESSING;

INTELLIGENT COMPUTING;

EID: 84883687716     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2499788.2499789     Document Type: Conference Paper

References (16)

1. P. Belhumeur, J. Hespanha, and D. Kriegman. Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE TPAMI, 19(7):711-720, 1997. (Pubitemid 127762263)
2. C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines. ACM TIST, 2(3):27:1-27:27, 2011.
3. K.-Y. Chang, C. S. Chen, and Y. P. Hung. Ordinal hyperplanes ranker with cost sensitivities for age estimation. In CVPR'11, pages 585-592. IEEE, 2011.
4. T. Cootes, G. Edwards, and C. Taylor. Active appearance models. In ECCV'98, volume 2, pages 484-498. Springer, 1998.
5. Y. Fu, Y. Xu, and T. Huang. Estimating human ages by manifold analysis of face pictures and regression on aging features. In ICME'07, pages 1383-1386. IEEE, 2007.
6. Y. F. G. Guo, G. Mu and T. Huang. Human age estimation using bio inspired features. In CVPR'09, pages 112-119. IEEE, 2009.
7. X. Geng, Z. Zhou, Y. Zhang, G. Li, and H. Dai. Learning from facial aging patterns for automatic age estimation. In ACM Multimedia, pages 307-316, 2006.
8. G. Guo, Y. Fu, C. Dyer, and T. Huang. Image-based human age estimation by manifold learning and locally adjusted robust regression. IEEE Trans. Image Processing, 17(7):1178-1188, 2008.
9. A. Lanitis, C. Draganova, and C. Christodoulou. Comparing different classifiers for automatic age estimation. IEEE Trans. Sys. Man Cyber. Part B, 34(1):621-628, Feb. 2004.
10. C. Li, Q. Liu, J. Liu, and H. Lu. Learning ordinal discriminative features for age estimation. In CVPR'12, pages 2570-2577. IEEE, 2012.
11. K. Ricanek and T. Tesafaye. MORPH: A longitudinal image database of normal adult age-progression. In AFGR'06, pages 341-345. IEEE, 2006.
12. The FG-NET Consortium. The FG-NET Aging Database [Online]. Available: http://www.fgnet.rsunit.com, 2012.
13. V. N. Vapnik. Statistical Learning Theory. New York, 1998.
14. S. Yan, H. Wang, T. Huang, and X. Tang. Ranking with uncertain labels. In ICME'07, pages 96-99. IEEE, 2007.
15. S. Yan, X. Zhou, M. Liu, M. Hasegawa-Johnson, and T. Huang. Regression from patch-kernel. In CVPR'08, pages 1-8. IEEE, 2008.
16. Y. Zhang and D. Yeung. Multi-task warped gaussian process for personalized age estimation. In CVPR'10, pages 2622-2629. IEEE, 2010.