This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces
March 2008 (vol. 30 no. 3)
pp. 541-547
ASCII Text
x 
 
Erno M?kinen, Roope Raisamo, "Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 3, pp. 541-547, March, 2008.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2007.70800,
author = {Erno M?kinen and Roope Raisamo},
title = {Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {30},
number = {3},
issn = {0162-8828},
year = {2008},
pages = {541-547},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2007.70800},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces
IS - 3
SN - 0162-8828
SP541
EP547
EPD - 541-547
A1 - Erno M?kinen,
A1 - Roope Raisamo,
PY - 2008
KW - Classifier design and evaluation
KW - Computer vision
KW - Face and gesture recognition
KW - Interactive systems
KW - Vision I/O
KW - Machine learning
VL - 30
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
We present a systematic study on gender classification with automatically detected and aligned faces. We experimented with 120 combinations of automatic face detection, face alignment and gender classification. One of the findings was that the automatic face alignment methods did not increase the gender classification rates. However, manual alignment increased classification rates a little, which suggests that automatic alignment would be useful when the alignment methods are further improved. We also found that the gender classification methods performed almost equally well with different input image sizes. In any case, the best classification rate was achieved with a support vector machine. A neural network and Adaboost achieved almost as good classification rates as the support vector machine and could be used in applications where classification speed is considered more important than the best possible classification accuracy.

[1] B. Moghaddam and M.-H. Yang, “Gender Classification with Support Vector Machines,” Proc. Int'l Conf. Automatic Face and Gesture Recognition, pp. 306-311, Mar. 2000.
[2] P.J. Phillips, H. Wechsler, J. Huang, and P.J. Rauss, “The FERET Database and Evaluation Procedure for Face Recognition Algorithms,” Image and Vision Computing J., vol. 16, no. 5, pp. 295-306, 1998.
[3] 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. 14-21, 2002.
[4] P. Viola and M.J. Jones, “Robust Real-Time Face Detection,” Int'l J.Computer Vision, vol. 57, no. 2, pp. 137-154, 2004.
[5] Y. Freund and R.E. Schapire, “A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting,” J. Computer Systems Science, vol. 55, no. 1, pp. 119-139, 1997.
[6] M. Castrillón-Santana, O. Déniz-Suárez, J. Hernández-Sosa, and A. Domínguez-Brito, “Identity and Gender Recognition Using the ENCARA Real-Time Face Detector,” Proc. Conf. Assoc. Espaola para la Inteligencia Artificial, 2003.
[7] M.F. Castrillón-Santana, “On Real-Time Face Detection in Video Streams: An Opportunistic Approach,” PhD dissertation, Universidad de Las Palmas de Gran Canaria, Mar. 2003.
[8] M. Castrillón-Santana, O. Déniz-Suárez, J. Lorenzo-Navarro, and M. Hernández-Tejera, “Gender and Identity Classification for a Naive and Evolving System,” Proc. Second Workshop Multimodal User Authentication, 2006.
[9] B. Wu, H. Ai, and C. Huang, “Real-Time Gender Classification,” Proc. Third Int'l Symp. Multispectral Image Processing and Pattern Recognition, vol. 5286, pp. 498-503, Oct. 2003.
[10] X. Xiao, “Face Detection and Retrieval,” master's thesis, Tsinghua Univ., 2002.
[11] T. Wang, H. Ai, and G. Huang, “A Two-Stage Approach to Automatic Face Alignment,” Proc. Third Int'l Symp. Multispectral Image Processing and Pattern Recognition, H. Lu and T. Zhang, eds., pp. 558-563, 2003.
[12] C. BenAbdelkader and P. Griffin, “A Local Region-Based Approach to Gender Classification from Face Images,” Proc. 2005 IEEE CS Conf. Computer Vision and Pattern Recognition, p. 52, 2005.
[13] Z. Yang, M. Li, and H. Ai, “An Experimental Study on Automatic Face Gender Classification,” Proc. 18th IEEE Int'l Conf. Pattern Recognition, vol. 3, pp. 1099-1102, Aug. 2006.
[14] S. Baluja and H.A. Rowley, “Boosting Sex Identification Performance,” Int'l J. Computer Vision, vol. 71, no. 1, pp. 111-119, 2007.
[15] “OpenCV 1.0, Open Source Computer Vision Library,” http://www.intel.com/technology/computing opencv/, 2006.
[16] T. Cootes and C. Taylor, Statistical Models of Appearance for Medical Image Analysis and Computer Vision, 2001.
[17] E. Mäkinen and R. Raisamo, “Real-Time Face Detection for Kiosk Interfaces,” Proc. Asia-Pacific Conf. Computer-Human Interaction 2002, pp.528-539, 2002.
[18] M.B. Stegmann, B.K. Ersbøll, and R. Larsen, “FAME—A Flexible Appearance Modelling Environment,” IEEE Trans. Medical Imaging, vol. 22, no. 10, pp. 1319-1331, 2003.
[19] T. Ojala, M. Pietikäinen, and D. Harwood, “A Comparative Study of Texture Measures with Classification Based on Featured Distributions,” Pattern Recognition, vol. 29, no. 1, pp. 51-59, 1996.
[20] C. Cortes and V. Vapnik, “Support-Vector Networks,” Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.
[21] C.-C. Chang and C.-J. Lin, “LIBSVM: A Library for Support Vector Machines,” http://www.csie.ntu.edu.tw/~cjlinlibsvm/, 2001.
[22] T. Ojala, M. Pietikäinen, and T. Mäenpää, “Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971-987, July 2002.
[23] H.-C. Lian and B.-L. Lu, “Multi-View Gender Classification Using Local Binary Patterns and Support Vector Machines,” Proc. Third Int'l Symp. Neural Networks, vol. 2, pp. 202-209, 2006.
[24] P. Viola and M. Jones, “Rapid Object Detection Using a Boosted Cascade of Simple Features,” Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 511-518, 2001.
[25] O. Jesorsky, K.J. Kirchberg, and R. Frischholz, “Robust Face Detection Using the Hausdorff Distance,” Proc. Third Int'l Conf. Audio and Video-Based Biometric Person Authentication, pp. 90-95, 2001.
[26] Y. Rodriguez, F. Cardinaux, S. Bengio, and J. Mariéthoz, “Measuring the Performance of Face Localization Systems,” Image and Vision Computing, vol. 24, no. 8, pp. 882-893, Aug. 2006.

Index Terms:
Classifier design and evaluation, Computer vision, Face and gesture recognition, Interactive systems, Vision I/O, Machine learning
Citation:
Erno M?kinen, Roope Raisamo, "Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 3, pp. 541-547, March 2008, doi:10.1109/TPAMI.2007.70800
Usage of this product signifies your acceptance of the Terms of Use.