The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.04 - April (2011 vol.33)
pp: 858-864
Juan Bekios-Calfa , Universidad Católica del Norte, Antofagasta
José M. Buenaposada , Universidad Rey Juan Carlos, Móstoles
Luis Baumela , Universidad Politécnica de Madrid, Boadilla del Monte
ABSTRACT
Emerging applications of computer vision and pattern recognition in mobile devices and networked computing require the development of resource-limited algorithms. Linear classification techniques have an important role to play in this context, given their simplicity and low computational requirements. The paper reviews the state-of-the-art in gender classification, giving special attention to linear techniques and their relations. It discusses why linear techniques are not achieving competitive results and shows how to obtain state-of-the-art performances. Our work confirms previous results reporting very close classification accuracies for Support Vector Machines (SVMs) and boosting algorithms on single-database experiments. We have proven that Linear Discriminant Analysis on a linearly selected set of features also achieves similar accuracies. We perform cross-database experiments and prove that single database experiments were optimistically biased. If enough training data and computational resources are available, SVM's gender classifiers are superior to the rest. When computational resources are scarce but there is enough data, boosting or linear approaches are adequate. Finally, if training data and computational resources are very scarce, then the linear approach is the best choice.
INDEX TERMS
Computer vision, gender classification, Fisher linear discriminant analysis.
CITATION
Juan Bekios-Calfa, José M. Buenaposada, Luis Baumela, "Revisiting Linear Discriminant Techniques in Gender Recognition", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.33, no. 4, pp. 858-864, April 2011, doi:10.1109/TPAMI.2010.208
REFERENCES
[1] B. Moghaddam and M.-H. Yang, "Learning Gender with Support Faces," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 707-711, May 2002.
[2] S. Baluja and H.A. Rowley, "Boosting Sex Identification Performance," Int'l J. Computer Vision, vol. 71, no. 1, pp. 111-119, Jan. 2007.
[3] E. Mäkinen and R. Raisamo, "Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 3, pp. 541-547, Mar. 2008.
[4] E. Mäkinen and R. Raisamo, "An Experimental Comparison of Gender Classification Methods," Pattern Recognition Letters, vol. 29, no. 10, pp. 1544-1556, July 2008.
[5] B.A. Golomb, D.T. Lawrence, and T.J. Sejnowski, "Sexnet: A Neural Network Identifies Sex from Human Faces," Advances in Neural Information Processing Systems, pp. 572-577, Morgan Kauffmann, 1990.
[6] G. Shakhnarovich, P.A. Viola, and B. Moghaddam, "A Unified Learning Framework for Real Time Face Detection and Classification," Proc. Int'l Conf. Automatic Face and Gesture Recognition, pp. 16-26, 2002.
[7] A. Lapedriza, M.J. Marin-Jiménez, and J. Vitrià, "Gender Recognition in Non Controlled Environments," Proc. Int'l Conf. Robotics and Automation, pp. 834-837, 2006.
[8] K. Fukunaga, Introduction to Statistical Pattern Recognition. Academic Press, 1990.
[9] J. Yang and J.-y. Yang, "Why Can LDA Be Performed in PCA Transformed Space?" Pattern Recognition, vol. 36, pp. 563-566, 2003.
[10] M. Zhu and A.M. Martínez, "Selecting Principal Components in a Two-Stage LDA Algorithm," Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. I, pp. 132-137, 2006.
[11] R. Johnson and D. Wichern, Applied Multivariate Statistical Analysis. Prentice-Hall, 1998.
[12] A.M. Martinez and A.C. Kak, "PCA versus LDA," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 228-233, Feb. 2001.
[13] M.A. Vicente, P.O. Hoyer, and A. Hyvärinen, "Equivalence of Some Common Linear Feature Extraction Techniques for Appearance-Based Object Recognition Tasks," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 5, pp. 896-900, May 2007.
[14] A. Jain and J. Huang, "Integrating Independent Components and Linear Discriminant Analysis for Gender Classification," Proc. Int'l Conf. Automatic Face and Gesture Recognition, pp. 159-163, 2004.
[15] P. Phillips, H. Moon, P. Rauss, and S. Rizvi, "The Feret Evaluation Methodology for Face Recognition Algorithms," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 10, pp. 1090-1104, Oct. 2000.
[16] M. Minear and D.C. Park, "A Lifespan Database of Adult Facial Stimuli," Behavior Research Methods, Instruments and Computers, vol. 36, pp. 630-633, 2004.
[17] P. Viola and M.J. Jones, "Robust Real-Time Face Detection," Int'l J. Computer Vision, vol. 57, no. 2, pp. 137-154, May 2004.
[18] J.C. Platt, "Fast Training of Support Vector Machines Using Sequential Minimal Optimization," Advances in Kernel Methods: Support Vector Learning, pp. 185-208, MIT Press, 1999.
[19] G. Guo, C.R. Dyer, Y. Fu, and T.S. Huang, "Is Gender Recognition Affected by Age?" Proc. IEEE Int'l Conf. Computer Vision Workshop Human-Computer Interaction, pp. 2032-2039, 2009.
[20] H. Ai and G. Wei, "Face Gender Classification on Consumer Images in a Multiethnic Environment," Proc. Conf. Advances in Biometrics, 2009.
[21] P. Zhang, J. Peng, and N. Riedel, "Discriminant Analysis: A Least Squares Approximation View," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2005.
[22] W. Zheng, L. Zhao, and C. Zou, "An Efficient Algorithm to Solve the Small Sample Size Problem for LDA," Pattern Recognition, vol. 37, pp. 1077-1079, 2004.
[23] J. Ye, R. Janardan, C.H. Park, and H. Park, "An Optimization Criterion for Generalized Discriminant Analysis on Undersample Problems," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 8, pp. 982-994, Aug. 2004.
14 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool