Sparse models for gender classification

Proceedings - Sixth IEEE International Conference on Automatic Face and Gesture Recognition

Volumn , Issue , 2004, Pages 201-206

  Costen, N P ^a   Brown, M ^a   Akamatsu, S ^b  

^a MANCHESTER METROPOLITAN UNIVERSITY   (United Kingdom)

^b HOSEI UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

FACIAL CODING; GENDER CLASSIFICATIONS; SUPPORT VECTOR MACHINES; TRAINING DATA;

EIGENVALUES AND EIGENFUNCTIONS; ERROR ANALYSIS; IMAGE CODING; INTERPOLATION; MATHEMATICAL MODELS; PARAMETER ESTIMATION; VECTORS; VOCABULARY CONTROL;

IMAGE PROCESSING;

EID: 4544286357     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/AFGR.2004.1301531     Document Type: Conference Paper

References (16)

1. P. S. Bradley and O. L. Magasarian. Feature selection via concave minimization and Support Vector Machines. In International Conference on Machine Learning, pages 82-90, 1998.
2. M. Brown. Exploring the set of sparse, optimal classifiers. International Workshop on Artifical Neural Networks in Pattern Recognition, 2003.
3. R. Brunelli and T. Poggio. Face recognition: Features vs. templates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(10): 1042-1052, Oct. 1993.
4. S. S. Chen, D. L. Donoho, and M. A. Saunders. Atomic decomposition by basis pursuit. SIAM Journal of Scientific Computing, 20(1):33-61, 1998.
5. T. F. Cootes, G. J. Edwards, and C. J. Taylor. Active Appearance Models. In European Conference on Computer Vision, volume 2, pages 484-498, 1998.
6. B. Edelman, D. Valentin, and H. Abdi. Sex classification of face areas: How well can a linear neural network predict human performance? Journal of Biological Systems, 6(3):241-264, 1998.
7. G. E. Edwards, C. J. Taylor, and T. F. Cootes. Learning to identify and track faces in image sequences. In British Machine Vision Conference, pages 130-139, 1997.
8. M. A. T. Figueiredo. Adaptive sparesness using Jeffreys prior. In Advances in Neural Information Processing System 14, pages 705-711, 2002.
9. B. A. Golomb, D. T. Lawrence, and T. J. Sejnowski. Sexnet: A neural network identifies sex from human faces. In Neural Information Processing Systems, pages 572-577, 1991.
10. G. Guo, S. Z. Li, and K. L. Chan. Support vector machines for face recognition. Image and Vision Computing, 19(9-10):631-638, 2001.
11. S. Gutta and P. J. Wechsler, H. and Phillips. Gender and ethnic classification. In IEEE International Conference on Automatic Face and Gesture Recognition, pages 194-199, 1998.
12. B. Moghadam and M. Yang. Learning gender with support faces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5):707-711, 2002.
13. R. M. Neal. Bayesian Learning for Neural Networks. Number 118 in Lecture Notes in Statistics. Springer-Verlag, NY, 1996.
14. P. J. Phillips. Support vector machines applied to face recognition. In Neural Information Processing Systems, pages 803-899, 1999.
15. R. Tibshirani. Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society B, 58(1):267-288, 1996.
16. V. N. Vapnik. Statistical Learning Theory. Wiley, New York, 1998.