CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Gender Detection using Machine Learning Techniques and Delaunay Triangulation

by Sarthak Gupta
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 124 - Number 6
Year of Publication: 2015
Authors: Sarthak Gupta
10.5120/ijca2015905507

Sarthak Gupta . Gender Detection using Machine Learning Techniques and Delaunay Triangulation. International Journal of Computer Applications. 124, 6 ( August 2015), 27-32. DOI=10.5120/ijca2015905507

@article{ 10.5120/ijca2015905507,
author = { Sarthak Gupta },
title = { Gender Detection using Machine Learning Techniques and Delaunay Triangulation },
journal = { International Journal of Computer Applications },
issue_date = { August 2015 },
volume = { 124 },
number = { 6 },
month = { August },
year = { 2015 },
issn = { 0975-8887 },
pages = { 27-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume124/number6/22110-2015905507/ },
doi = { 10.5120/ijca2015905507 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:13:41.907480+05:30
%A Sarthak Gupta
%T Gender Detection using Machine Learning Techniques and Delaunay Triangulation
%J International Journal of Computer Applications
%@ 0975-8887
%V 124
%N 6
%P 27-32
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data mining today is being used widely in diverse areas. For example: fraudulent systems, recommender systems, disease prediction, and numerous other applications. One such application is exploited in this article. This paper presents an approach to detect gender of a person through frontal facial image, using techniques of data mining and Delaunay triangulation. Gender prediction can prove to be a very useful technique in HCI (Human Computer Interaction) Systems. Classification, being a very power technique in data mining to group categorical data, is used here to classify a gender as either male, or female. Various classification algorithms such as Functional Trees, AdaBoost, J48, and few others are used to gauge the maximum accuracy. The model used in this paper is robust and attains accuracy level of 93.8283% along with relative scale invariance. Details of the prediction model and results are reported herein.

References
  1. H. C. Kim, D. Kim, Z. Ghahramani, S. Y. Bang, “Appearance-based gender classification with gaussian processes,” Pattern Recognition Letters, Vol. 27, No. 6, pp. 618–626, 2006.
  2. F. Ahmed, M. H. Kabir, “Facial feature representation with directional ternary pattern (dtp): Application to gender classification,” in Proceedings of the IEEE International Conference on Information Reuse and Integration, USA, 2012, pp. 159–164.
  3. Viola, P. & Jones, M., 2001. Rapid object detection using a boosted cascade of simple features. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001. CVPR 2001., 1, pp.511-518.
  4. Huang, C. et al., 2004. Boosting nested cascade detector for multi-view face detection. Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2, pp.415-418.
  5. Yang, Z., Ming, L. &Haizhou, A., 2006. An Experimental Study on Automatic Face Gender Classification. 18th International Conference on Pattern Recognition, 2006. ICPR 2006., pp.1099-1102.
  6. H. C. Kim, D. Kim, Z. Ghahramani, S. Y. Bang, “Appearance-based gender classification with Gaussian processes,” Pattern Recognition Letters, Vol. 27, No. 6, pp. 618–626, 2006.
  7. F. Ahmed, H. Bari, E. Hossain, “Person-independent facial expression recognition based on compound local binary pattern (clbp),” International Arab Journal of Information Technology, Vol. 11, No. 2, 2013.
  8. Yi Xiao, Hong Yan, ‎2002, Facial Feature Location with Delaunay Triangulation/Voronoi Diagram Calculation, School of Electrical & Information Engineering, University of Sydney, NSW 2006, Australia
  9. S. Baluja and H. A. Rowley. Boosting sex identification performance. IJCV, 71(1):111–119, 2007.
  10. B. Moghaddam and M.-H. Yang. Learning gender with support faces. IEEE TPAMI, 24:707–711, 2002.
  11. W. Gao and H. Ai. Face gender classification on consumer images in a multiethnic environment. In ICB, pages 169– 178, 2009.
  12. RoytatsuIga, Kyoko Izumi, Hisanori Hayashi, GentaroFukano and TestsuyaOhtani, “Gender and Age Estimation from Face Images”, International Conference on The Society of Instrument and Control Engineering, pp. 756-761, August, 2003.
  13. Hui-Cheng Lain and Bao-Liang Lu, “Multi-View Gender Classification using Local Binary Patterns and Support Vector Machines”, International Conference on Neural Networks, pp. 202-209, 2006.
  14. Md. Hafizur Rahman et al., 2013, "Face Detection and Sex Identification from Color Images using AdaBoost with SVM based Component Classifier", International Journal of Computer Applications (0975 – 8887), Volume 76– No.3, August 2013
  15. Y. Saatci and C. Town. Cascaded classification of gender and facial expression using active appearance models. In FGR, 2006.
  16. Z. Xu, L. Lu, and P. Shi. A hybrid approach for gender classification from face images. In ICPR, 2008.
  17. Gundimada, S. and Asari, V., (2004), Face Detection Technique Based on Rotation Invariant Wavelet Features, Proceedings of ITCC International Conference on Information Technology: Coding and Computing, pp: 157 – 158
  18. Hsu, R. L., Abdel-Mottaleb, M. and Jain, A. K. (2002), Face Detection in Color Images, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 5, pp: 696-706.
  19. Xie, X. and Kin-Man, L. (2004), An Efficient Illumination Compensation Scheme for Face Recognition. 8th Control, Automation, Robotics and Vision Conference (ICARCV 2004), pp: 1240 – 1243
  20. Pai, Y., Ruan, S., Shie, M. and Liu, Y. (2006), A Simple and Accurate Color Face Detection Algorithm in Complex Background, IEEE International Conference on Multimedia and Expo. Toronto, ON, Canada, pp: 1545-1548.
  21. Gundimada, S. Li Tao and Asari, V. (2004), Face Detection Technique Based on Intensity and Skin Color Distribution, ICIP '04. International Conference on Image Processing, Vol.2, pp: 1413 – 1416
  22. Ahmed M. Mharib et al., 2011, The Impact of Light Compensation on the Performance of Parametric Skin Detection Model, International Journal of Signal Processing, Image Processing and Pattern RecognitionVol. 4, No. 4, December, 2011
  23. Matthew Fisher et al., 2006, An Algorithm for the Construction of Intrinsic Delaunay Triangulations with Applications to Digital Geometry Processing, SIGGRAPH 2006
  24. SwathiKalam et al. Gender Classification using Geometric Facial Features, International Journal of Computer Applications (0975 – 8887), Volume 85 – No 7, January 2014
  25. JOA˜ O GAMA, 2004, Functional Trees, University of Porto, Rua Campo Alegre 823, 4150 Porto, Portugal, Machine Learning, 55, 219–250, 2004
  26. XindongWu et al., 2008, Top 10 algorithms in data mining, Springer, KnowlInfSyst (2008) 14:1–37
  27. FEI face database, http://fei.edu.br/~cet/facedatabase.html
Index Terms

Computer Science
Information Sciences

Keywords

Functional Trees J48 Naïve Bayes Machine Learning Gender Classification WEKA Machine learning

Powered by PhDFocusTM