We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

A Hybrid Approach for Gender Classification of Web Images

by Muhammad Usman Khan, Hafiz Adnan Habib, Nasir Saleem
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 54 - Number 7
Year of Publication: 2012
Authors: Muhammad Usman Khan, Hafiz Adnan Habib, Nasir Saleem
10.5120/8577-2316

Muhammad Usman Khan, Hafiz Adnan Habib, Nasir Saleem . A Hybrid Approach for Gender Classification of Web Images. International Journal of Computer Applications. 54, 7 ( September 2012), 11-16. DOI=10.5120/8577-2316

@article{ 10.5120/8577-2316,
author = { Muhammad Usman Khan, Hafiz Adnan Habib, Nasir Saleem },
title = { A Hybrid Approach for Gender Classification of Web Images },
journal = { International Journal of Computer Applications },
issue_date = { September 2012 },
volume = { 54 },
number = { 7 },
month = { September },
year = { 2012 },
issn = { 0975-8887 },
pages = { 11-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume54/number7/8577-2316/ },
doi = { 10.5120/8577-2316 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:55:04.701551+05:30
%A Muhammad Usman Khan
%A Hafiz Adnan Habib
%A Nasir Saleem
%T A Hybrid Approach for Gender Classification of Web Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 54
%N 7
%P 11-16
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In recent times, gender recognition of facial images has achieved lots of attraction. It can be useful in many places e. g. security, web searching, human computer interaction etc. In this paper, an approach containing both face detection and gender classification tasks has been proposed. In face detection part, Haar features have been chosen to present appearance features along with Ada-Boost technique to target strong and powerful features in cascaded form. For gender classification, Bayesian Classifier has been used where image is analyzed in blocks/patches form. The blocking technique is same as used in DCT approach. Experimental results have shown that proposed approach is effective and robust with changes in pose (some degree), expressions and illumination.

References
  1. Ziyi Xu, Li Lu and Pengfei Shi, "A Hybrid Approach to Gender Classification from Face Images", Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China, IEEE, 2008.
  2. A. Golomb, D. T. Lawrence, and T. j. Sejnowski, "SEXNET: A neural network identifies sex from human faces", Neural Information Processing Systems, 1991, pp: 572-577.
  3. G. w. Cottrell and J. Metcalfe. EMPATH: "Face, emotion, and gender recognition using holons". Neural Information Processing Systems, 1991, pp: 564-571.
  4. Lowe David G. , "Object Recognition from Local Scale-Invariant Features", International Conference on Computer Vision, Canada, 1999.
  5. Podilchuk and X. Zhang, "Face Recognition using DCT Based Feature Vectors", In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, Vol. 4, Pp. 2144-2147, 1996.
  6. V. Algazi, K. Brown, and M. Ready, "Transform representation of the spectra of acoustic speech segments with appliances, part I: General approach and application to speech recognition," IEEE Trans. Speech Audio Process. , vol. 1, pp. 180–195, 1993.
  7. L. Sirovich and L. Keefe, "Low dimensional procedure for characterization of human faces," J. Opt. Soc. Amer. , vol. 4, pp. 519–524, 1987.
  8. F. H. Tivive and A. Bouzerdoum, "A Gender Recognition System Using Shunting Inhibitory Convolutional Neural Networks", Faculty of Informatics, University of Wollongong, 2006.
  9. P. Latha, Dr. L. Ganesan, Dr. S. Annadurai, "Face Recognition using Neural Networks", Signal Processing: An International Journal (SPIJ), Volume 3, 153-160, 2009.
  10. Costen, N. , Brown, M. , Akamatsu, S. , "Sparse models for gender classification". Proc. Internat. Conf. on Automatic Face and Gesture Recognition (FGR'04), 2004, pp: 201–206.
  11. Shakhnarovich, G. , Viola, P. A. , Moghaddam, B. , "A unified learning framework for real time face detection and classification", Proc. Internat. Conf. on Automatic Face and Gesture Recognition (FGR'02). IEEE, 2002, pp: 14–21.
  12. B. Wu, H. Ai, C. Huang, "Real Time Gender Classification", Multi-spectral Image Processing and Pattern Recognition, 2003.
  13. Paul Viola, Michael Jones, "Rapid Object Detection using a Boosted Cascade of Simple Features", 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'01), p. 511, Volume 1, 2001.
  14. Simon J. D. Prince and Jania Aghajanian, "Gender Classification in Uncontrolled Settings Using Additive Logistic Models", Department of Computer Science, University College London.
  15. J. Wu,W. Smith, and E. Hancock, "Gender classification using shape from shading", In BMVC 2007, pages 499–508, 2007.
  16. Zhiming Liu, Jian Yang, Chengjun Liu, "Extracting Multiple Features in the CID Color Space for Face Recognition", Dept. of Computer Science, New Jersey Institute of Technology, USA, IEEE Explore, April 2010, pages 2502 – 2509.
  17. B. Moghaddam and M. Yang, "Learning Gender with Support Faces", PAMI, pp. 707–711, 2002.
  18. A. A. Efros andW. T. Freeman, "Image quilting for texture synthesis and transfer", Proc. SIGGRAPH, pp. 341-346, 2000.
  19. P. Felzenszwalb, D. McAllester and D. Ramanan, "A discriminatively trained, multi-scale, deformable part model", CVPR, pp. 1-8, 2008.
Index Terms

Computer Science
Information Sciences

Keywords

Haar Features Ada-Boost Bayesian Classifier DCT (Discrete Cosine Transform)

Powered by PhDFocusTM