Real-time gender recognition for uncontrolled environment of real-life images

VISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications

Volumn 2, Issue , 2010, Pages 357-362

  Chen, Duan Yu ^a   Lin, Kuan Yi ^a  

^a YUAN ZE UNIVERSITY   (Taiwan)

Author keywords

Gender recognition; Real life images; Uncontrolled environment

Indexed keywords

DATA SETS; FACE DETECTION; GENDER RECOGNITION; HUMAN SUBJECTS; REAL-LIFE IMAGES; SKIN COLOR; SURVEILLANCE VIDEO; UNCONTROLLED ENVIRONMENT; VIEWING ANGLE;

COMPUTER VISION; DECISION MAKING;

SECURITY SYSTEMS;

EID: 77956304463     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

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 Garc'ia-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 Natural Computation.
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.
11. 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.
12. Wu, B., Ai, H. and Huang, C, 2003. LUT-Based Adaboost for Gender Classification. Audio- and Video-BasedBiometric Person Authentication
13. Wu, B., Ai, H. and Huang, C, 2004. Facial image retrieval based on demographic classification. ICPR'04, International Conference on Pattern Recognition.
14. Lu, H., Huang, Y., Chen, Y. and Yang, D., 2003. Automatic gender recognition based on pixel-pattern-based texture feature. Journal of Real-Time Image Processing.
15. Nikolaus, R., 2007. Learning the Parts of Objects using Non-negative Matrix Factorization. Term Paper, Feb 2007.
16. Lee, D.D. and Seung, H.S., 1999. Learning the parts of objects with nonnegative matrix factorization. Nature.
17. Lapedriza, A., Masip, D. and Vitria, J., 2005. Are External Face Features Useful for Automatic Face Classification? CVPR'05, Computer Vision and Pattern Recognition.
18. Lapedriza, A., Marin-Jimenez, M.J. and Vitria, J., 2006. Gender Recognition in Non Controlled Environments. International Conference on Pattern Recognition.
19. 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.
20. Osuna, E. and Freund, R., 1997. An Improved training algorithm for Support Vector Machine. IEEE Workshop on Neural Networks for Signal Processing.
21. Moghaddam, B. and Yang, M.H., 2000. Gender Classification with Support Vector Machines. Int'l Conf. on Automatic Face and Gesture Recognition.
22. Freund, Y. and Schapire, R. E., 1996. Experiments with a New Boosting Algorithm. Machine Learning: Proceedings of the Thirteenth International Conferenc.
23. Schapire, R. E. and Singer, Y., 1999. Improved boosting algorithms using confidence-rated predictions. Machine. Learning.
24. Viola, P. and Jones, M., 2001. Rapid object detection using a boosted cascade of simple features. IEEE Conference on Computer Vision and Pattern Recognition.