REAL-TIME GENDER RECOGNITION FOR UNCONTROLLED ENVIRONMENT OF REAL-LIFE IMAGES

Duan-Yu Chen, Kuan-Yi Lin

2010

Abstract

Gender recognition is a challenging task in real life images and surveillance videos due to their relatively low-resolution, under uncontrolled environment and variant viewing angles of human subject. Therefore, in this paper, a system of real-time gender recognition for real life images is proposed. The contribution of this work is fourfold. A skin-color filter is first developed to filter out non-face noises. In order to make the system robust, a mechanism of decision making based on the combination of surrounding face detection, context-regions enhancement and confidence-based weighting assignment is designed. Experimental results obtained by using extensive dataset show that our system is effective and efficient in recognizing genders for uncontrolled environment of real life images.

References

  1. Wang, Y., Ai, H., and Wu, B., Huang, C., 2004. Real Time Facial Expression Recognition with Adaboost. ICPR'04, International Conference on Pattern Recognition.
  2. Andreu, Y., Mollineda, R.A., and García-Sevilla, P., 2009. Gender Recognition from a Partial View of the Face Using Local Feature Vectors. Lecture Notes in Computer Science.
  3. Gallagher, A.C. and Chen, T., 2009. Understanding Images of Groups of People. CVPR'09, IEEE Conference on Computer Vision and Pattern Recognition.
  4. Cao, L., Dikmen, M., Fu, Y., and Huang, T.S., 2008. Gender Recognition from Body. ACM international conference on Multimedia.
  5. Shen, B.C., Chen, C.S., and Hsu, H.H., 2009. Fast Gender Recognition by Using A shared-Integral-Image Approach. ICASSP'09, IEEE International Conference on Acoustics, Speech and Signal Processing.
  6. Lu, H. and Lin, H., 2007. Gender Recognition using Adaboosted Feature. International Conference on
  7. Lin, H., Lu, H., and Zhang, L., 2006. A New Automatic Recognition System of Gender, Age and Ethnicity. The Sixth World Congress on Intelligent Control and Automation.
  8. Balci, K. and Atalay, V., 2002. PCA for gender estimation: which eigenvectors contribute? ICPR'02, International Conference on Pattern Recognition.
  9. Rodrigo, V., Javier, R. D. S., and Mauricio, C., 2006. Gender Classification of Faces Using Adaboost. CIARP'06.
  10. Fang, Y. and Wang, Z., 2008. Improving LBP Features for Gender Classification. International Conference on Wavelet Analysis and Pattern Recognition. Lian, H.C. and Lu, B.L., 2007. Multi-View Gender Classification Using Multi-Resolution Local Binary Patterns and Support Vector Machines. International Journal of Neural Systems.
  11. Wu, B., Ai, H. and Huang, C., 2003. LUT-Based Adaboost for Gender Classification. Audio- and Video-Based Biometric Person Authentication.
  12. Wu, B., Ai, H. and Huang, C., 2004. Facial image retrieval based on demographic classification. ICPR'04, International Conference on Pattern Recognition.
  13. Lu, H., Huang, Y., Chen, Y. and Yang, D., 2003.
  14. Nikolaus, R., 2007. Learning the Parts of Objects using Non-negative Matrix Factorization. Term Paper, Feb 2007.
  15. Lee, D.D. and Seung, H.S., 1999. Learning the parts of objects with nonnegative matrix factorization. Nature.
  16. Lapedriza, A., Masip, D. and Vitria, J., 2005. Are External Face Features Useful for Automatic Face Classification? CVPR'05, Computer Vision and Pattern Recognition.
  17. Lapedriza, A., Marin-Jimenez, M.J. and Vitria, J., 2006. Gender Recognition in Non Controlled Environments. International Conference on Pattern Recognition.
  18. Mayo, M. and Zhang, E., 2008. Improving Face Gender Classification By Adding Deliberately Misaligned Faces to The Training Data. Image and Vision Computing New Zealand.
  19. Osuna, E. and Freund, R., 1997. An Improved training algorithm for Support Vector Machine. IEEE Workshop on Neural Networks for Signal Processing.
  20. Moghaddam, B. and Yang, M.H., 2000. Gender Classification with Support Vector Machines. Int'l Conf. on Automatic Face and Gesture Recognition. Freund, Y. and Schapire, R. E., 1996. Experiments with a New Boosting Algorithm. Machine Learning: Proceedings of the Thirteenth International Conferenc.
  21. Schapire, R. E. and Singer, Y., 1999. Improved boosting algorithms using confidence-rated predictions. Machine. Learning.
  22. Viola, P. and Jones, M., 2001. Rapid object detection using a boosted cascade of simple features. IEEE Conference on Computer Vision and Pattern Recognition.
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Paper Citation


in Harvard Style

Chen D. and Lin K. (2010). REAL-TIME GENDER RECOGNITION FOR UNCONTROLLED ENVIRONMENT OF REAL-LIFE IMAGES . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010) ISBN 978-989-674-029-0, pages 357-362. DOI: 10.5220/0002823203570362


in Bibtex Style

@conference{visapp10,
author={Duan-Yu Chen and Kuan-Yi Lin},
title={REAL-TIME GENDER RECOGNITION FOR UNCONTROLLED ENVIRONMENT OF REAL-LIFE IMAGES},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)},
year={2010},
pages={357-362},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002823203570362},
isbn={978-989-674-029-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)
TI - REAL-TIME GENDER RECOGNITION FOR UNCONTROLLED ENVIRONMENT OF REAL-LIFE IMAGES
SN - 978-989-674-029-0
AU - Chen D.
AU - Lin K.
PY - 2010
SP - 357
EP - 362
DO - 10.5220/0002823203570362