Soft biometrics for video surveillance

Intelligent Video Surveillance: Systems and Technology

Volumn , Issue , 2009, Pages 407-431

  Fu, Yun ^a   Guo, Guodong ^b   Huang, Thomas S ^c  

^a the State University of New York   (United States)

^b West Virginia University   (United States)

^c UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84908703849     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Chapter

References (82)

1. Alley, T.R. 1988. Social and Applied Aspects of Perceiving Faces, Lawrence Erlbaum Associates, Hillsdale, NJ.
2. Baluja, S. and Rowley, H.A. 2007. Boosting sex identification performance. International Journal of Computer Vision 71(1):111-119.
3. Breiman, L. 2001. Random forests. Machine Learning 45(1):5-32.
4. Brunelli, R. and Poggio, T. 1992. HyperBF networks for gender classification. In DARPA Image Understanding Workshop, San Diego, CA, pp. 311-314.
5. Cai, D., He, X., Han, J., and Zhang, H.-J. 2006. Orthogonal Laplacianfaces for face recognition. IEEE Transactions on Image Processing 15(11):3608-3614.
6. Canny, J. 1986. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(6):679-698.
7. Cao, L., Dikmen, M., Fu, Y., and Huang, T.S. 2008. Gender recognition from body. In The ACM Conference on Multimedia, Vancouver, Canada, pp. 725-728.
8. Cootes, T., Edwards, G., and Taylor, C. 2001. Active appearance models. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(6):681-685.
9. Costen, N.P., Brown, M., and Akamastu, S. 2004. Sparse models for gender classification. In IEEE International Conference on Automatic Face and Gesture Recognition, Seoul, Korea.
10. Dalal, N. and Triggs, B. 2005. Histograms of oriented gradients for human detection. In IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, pp. 886-893.
11. Electronic Customer Relationship Management (ECRM), http://en.wikipedia.org/wiki/ECRM
12. Farkas, L. 1994. Anthropometry of the Head and Face, Raven Press, New York.
13. Freund, Y. and Schapire, R. 1996. Experiments with a new boosting algorithm. In International Conference on Machine Learning, Desenzano sul Garda, Italy.
14. Fu, Y. 2008. Unified discriminative subspace learning for multimodality image analysis. PhD thesis, University of Illinois at Urbana-Champaign, Urbana, IL.
15. Fu, Y., Guo, G., and Huang, T.S. 2009. Age synthesis and estimation via faces: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, to appear.
16. Fu, Y. and Huang, T.S. 2005. Locally linear embedded eigenspace analysis. Technical Report IFP-TR, University of Illinois at Urbana-Champaign, Urbana, IL, http://www.ifp.uiuc.edu/~yunfu2/papers/LEA-Yun05.pdf
17. Fu, Y. and Huang, T.S. 2008a. Human age estimation with regression on discriminative aging manifold. IEEE Transactions on Multimedia 10(4):578-584.
18. Fu, Y. and Huang, T.S. 2008b. Image classification using correlation tensor analysis. IEEE Transactions on Image Processing 17(2):226-234.
19. Fu, Y., Li, Z., Huang, T.S., and Katsaggelos. A.K. 2008a. Locally adaptive subspace and similarity metric learning for visual clustering and retrieval. Computer Vision and Image Understanding 110(3):390-402.
20. Fu, Y., Li, Z., Yuan, J., Wu, Y., and Huang, T.S. 2008b. Locality versus globality: Querydriven localized linear models for facial image computing. IEEE Transactions on Circuits and Systems for Video Technology 18(12):1741-1752.
21. Fu, Y., Xu, Y., and Huang, T.S. 2007. Estimating human age by manifold analysis of face pictures and regression on aging features. In IEEE International Conference on Multimedia and Expo, Beijing, China, pp. 1383-1386.
22. Fu, Y., Yan, S., and Huang, T.S. 2008c. Classification and feature extraction by simplexization. IEEE Transactions on Information Forensics and Security 3(1):91-100.
23. Fu, Y., Yan, S., and Huang, T.S. 2008d. Correlation metric for generalized feature extraction. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(12):2229-2235.
24. Geng, X., Zhou, Z.-H., and Smith-Miles, K. 2007. Automatic age estimation based on facial aging patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(12):2234-2240.
25. Geng, X., Zhou, Z.-H., Zhang, Y., Li, G., and Dai, H. 2006. Learning from facial aging patterns for automatic age estimation. In ACM Conference on Multimedia, Santa Barbara, CA, pp. 307-316.
26. Golomb, B.A., Lawrence, D.T., and Sejnowski, T.J. 1991. Sexnet: A neural network identifies sex from human faces. In Advances in Neural Information Processing Systems, Denver, CO, pp. 572-577.
27. Graf, A.B.A. and Wichmann, F.A. 2002. Gender classification of human faces. In International Workshop on Biologically Motivated Computer Vision, Tübingen, Germany, pp. 491-500.
28. Gunay, A. and Nabiyev, V.V. 2007. Automatic detection of anthropometric features from facial images. In IEEE Conference on Signal Processing and Communications Applications, Eskişehir, Turkey.
29. Guo, G., Dyer, C., Fu, Y., and Huang, T.S. 2009d. Is gender recognition affected by age? In IEEE International Conference on Computer Vision, Workshop on Human-Computer Interaction, Kyoto, Japan.
30. Guo, G., Fu, Y., Dyer, C., and Huang, T.S. 2008a. Image-based human age estimation by manifold learning and locally adjusted robust regression. IEEE Transactions on Image Processing 17(7):1178-1188.
31. Guo, G., Fu, Y., Huang, T.S., and Dyer, C. 2008b. A probabilistic fusion approach to human age prediction. In IEEE CVPR’08-SLAM Workshop, Anchorage, AK.
32. Guo, G., Mu, G., Fu, Y., Dyer, C., and Huang, T.S. 2009a. A study on automatic age estimation using a large database. In IEEE International Conference on Computer Vision, Kyoto, Japan.
33. Guo, G., Mu, G., Fu, Y., and Huang, T.S. 2009b. Human age estimation using bio-inspired features. In IEEE Conference on Computer Vision and Pattern Recognition, Miami Beach, Florida.
34. Guo, G., Mu, G., and Fu, Y. 2009c. Gender from body: A biologically-inspired approach with manifold learning. In The Ninth Asian Conference on Computer Vision, Xi’an, China.
35. Gutta, S., Wechsler, H., and Phillips, P. 1998. Gender and ethnic classification. In IEEE International Workshop on Automatic Face and Gesture Recognition, Nara, Japan, pp. 194-199.
36. Han, J. and Bhanu, B. 2006. Individual recognition using gait energy image. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(2): 316-322.
37. Hayashi, J., Yasumoto, M., Ito, H., and Koshimizu, H. 2001. Method for estimating and modeling age and gender using facial image processing. In Seventh International Conference on Virtual Systems and Multimedia, Berkeley, CA, pp. 439-448.
38. Hayashi, J., Yasumoto, M., Ito, H., Niwa, Y., and Koshimizu, H. 2002. Age and gender estimation from facial image processing. SICE Annual Conference, Osaka, Japan, 1:13-18.
39. He, X. and Niyogi, P. 2003. Locality preserving projections. In Advances in Neural Information Processing Systems, Whistler, BC.
40. Hotelling, H. 1936. Relations between two sets of variates. Biometrika 8:321-377.
41. Jain, A.K., Dass, S.C., and Nandakumar, K. 2004. Soft biometric traits for personal recognition systems. In International Conference on Biometric Authentication, Hong Kong, pp. 731-738.
42. Jain, A. and Huang, J. 2004. Integrating independent component analysis and linear discriminant analysis for gender classification. In IEEE International Conference on Automatic Face and Gesture Recognition, Seoul, Korea.
43. Kanno, T., Akiba, M., Teramachi, Y., Nagahashi, H., and Agui, T. 2001. Classification of age group based on facial images of young males by using neural networks. IEICE Transactions on Information and Systems E84-D(8):1094-1101.
44. Kwon, Y. and Lobo, N. 1999. Age classification from facial images. Computer Vision and Image Understanding 74(1):1-21.
45. Lanitis, A., Draganova, C., and Christodoulou, C. 2004. Comparing different classifiers for automatic age estimation. IEEE Transactions on Systems, Man and Cybernetics Part B 34(1):621-628.
46. Lanitis, A., Taylor, C., and Cootes, T. 2002. Toward automatic simulation of aging effects on face images. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(4):442-455.
47. Lee, L. 2003. Gait analysis for classification. Technical Report 2003-014. MIT AI Lab.
48. Lee, L. and Grimson, W.E.L. 2002. Gait analysis for recognition and classifi-cation. In IEEE International Conference on Automatic Face and Gesture Recognition, Washington, DC, pp. 155-162.
49. Li, X., Maybank, S.J., Yan, S., Tao, D., and Xu, D. 2008. Gait components and their application to gender recognition. IEEE Transactions on Systems, Man and Cybernetics Part C 38(2):145-155.
50. Ling, H., Soatto, S., Ramanathan, N., and Jacobs, D. 2007. A study of face recognition as people age. In IEEE International Conference on Computer Vision, Rio de Janeiro, Brazil.
51. Mark, L.S., Pittenger, J.B., Hines, H., Carello, C., Shaw, R.E., and Todd, J.T. 1980. Wrinkling and head shape as coordinated sources of age-level information. Perception and Psychophysics 27(2):117-124.
52. Moghaddam, B. and Yang, M.-H. 2002, Learning gender with support faces. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5): 707-711.
53. MORPH face database, http://faceaginggroup.com/
54. Oren, M., Papageorgiou, C., Sinha, P., Osuna, E., and Poggio, T. 1997. Pedestrian detection using wavelet templates. In IEEE Conference on Computer Vision and Pattern Recognition, San Juan, Puerto Rico, pp. 193-199.
55. Patterson, E., Ricanek, K., Albert, M., and Boone, E. 2006. Automatic representation of adult aging in facial images. In The IASTED International Conference on Visualization, Imaging, and Image Processing, Palma de Mallorca, Spain, pp. 171-176.
56. Patterson, E., Sethuram, A., Albert, M., Ricanek, K., and King, M. 2007. Aspects of age variation in facial morphology affecting biometrics. In IEEE Conference on Biometrics: Theory, Applications, and Systems, Washington, DC.
57. Phillips, P., Moon, H., Rizvi, S., and Rauss, P. 2000. The feret evaluation methodology for face recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10):1090-1104.
58. Ramanathan, N. and Chellappa, R. 2006a. Face verification across age progression. IEEE Transactions on Image Processing 15(11):3349-3361.
59. Ramanathan, N. and Chellappa, R. 2006b. Modeling age progression in young faces. In IEEE Conference on Computer Vision and Pattern Recognition, Southampton, U.K., pp. 387-394.
60. Ramanathana, N., Chellappa, R., and Biswas, S. 2009. Computational methods for modeling facial aging: A survey. Journal of Visual Languages and Computing 20(3): 131-144.
61. Ricanek, K. and Boone, E. 2005. The Effect of normal adult aging on standard PCA face recognition accuracy rates. In International Joint Conference on Neural Networks, Montreal, Canada, pp. 2018-2023.
62. Ricanek, K., Boone, E., and Patterson, E. 2006. Craniofacial aging on the Eigenface biometric. In The IASTED International Conference on Visualization, Imaging, and Image Processing, Palma de Mallorca, Spain, pp. 249-253.
63. Ricanek, K. and Tesafaye, T. 2006. MORPH: A longitudinal image database of normal adult age-progression. In IEEE International Conference on Automatic Face and Gesture Recognition, Southampton, U.K., pp. 341-345.
64. Sarkar, S., Phillips, P., Liu, Z., Vega, I., Grother, P., and Bowyer, K. 2005. The humanid gait challenge problem: Data sets, performance, and analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(2): 162-177.
65. Shakhnarovich, G., Viola, P.A., and Moghaddam, B. 2002. A unified learning framework for real time face detection and classification. In IEEE International Conference on Automatic Face and Gesture Recognition, Washington, DC, pp. 14-21.
66. Shan, C., Gong, S., McOwan, P.W. 2008. Fusing gait and face cues for human gender recognition. Neurocomputing 71(10-12):1931-1938.
67. Sun, Z., Bebis, G., Yuan, X., and Louis, S.J. 2002. Genetic feature subset selection for gender classification: a comparison study. In IEEE Workshop on Application of Computer Vision, Orlando, FL.
68. The FG-NET Aging database, http://www.fgnet.rsunit.com/http://www-prima. inrialpes.fr/FGnet/
69. Ueki, K., Hayashida, T., and Kobayashi, T. 2006. Subspace-based age-group classification using facial images under various lighting conditions. In IEEE International Conference on Automatic Face and Gesture Recognition, Southampton, U.K.
70. Video Surveillance, http://www.videosurveillance.com/
71. Viola, P. and Jones, M. 2004. Robust real-time face detection. International Journal of Computer Vision 57(2):137-154.
72. Wu, B., Ai, H., Huang, C., and Lao, S. 2003. LUT-based Adaboost for gender classification. In International Conference on Audio and Video-Based Biometric Person Authentication, Guildford, U.K.
73. Xu, X. and Huang, T.S. 2007. Soda-boosting and its application to gender recognition. In IEEE International Workshop on Analysis and Modeling of Faces and Gestures. Lecture Notes in Computer Science, Vol. 4778, Springer-Verlag, Berlin, pp. 193-204.
74. Yan, S., Liu, M., and Huang, T.S. 2008a. Extracting age information from local spatially flexible patches. In IEEE International Conference on Acoustics, Speech, and Signal Processing, Las Vegas, NV, pp. 737-740.
75. Yan, S., Wang, H., Huang, T.S., and Tang, X. 2007a. Ranking with uncertain labels. In IEEE International Conference on Multimedia and Expo, Beijing, China, pp. 96-99.
76. Yan, S., Wang, H., Tang, X., and Huang, T.S. 2007b. Learning auto-structured regressor from uncertain nonnegative labels. In IEEE International Conference on Computer Vision, Rio de Janeiro, Brazil.
77. Yan, S., Zhou, X., Liu, M., Hasegawa-Johnson, M., and Huang, T.S. 2008b. Regression from patch-kernel. In IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK.
78. Yang, Z. and Ai, H. 2007. Demographic classification with local binary patterns. In International Conference on Biometrics, Seoul, Korea, pp. 464-473.
79. Yoo, J.-H., Hwang, D., and Nixon, M.S. 2005. Gender classification in human gait using support vector machine. In Proceedings of Advanced Concepts for Intelligent Vision Systems, Antwerp, Belgium, pp. 138-145.
80. Yu, S., Tan, D., and Tan, T. 2006. A framework for evaluating the effect of view angle clothing and carrying condition on gait recognition. In International Conference on Pattern Recognition, Hong Kong, pp. 441-444.
81. Zebrowitz, L.A. 1997. Reading Faces: Window to the Soul? Westview Press, Boulder, CO.
82. Zhou, S.K., Georgescu, B., Zhou, X.S., and Comaniciu, D. 2005. Image based regression using boosting method. In IEEE International Conference on Computer Vision, Beijing, China, pp. 541-548.