CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

A Gender Recognition System from Facial Image

by Md. Nurul Ahad Tawhid, Emon Kumar Dey
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 180 - Number 23
Year of Publication: 2018
Authors: Md. Nurul Ahad Tawhid, Emon Kumar Dey
10.5120/ijca2018915852

Md. Nurul Ahad Tawhid, Emon Kumar Dey . A Gender Recognition System from Facial Image. International Journal of Computer Applications. 180, 23 ( Feb 2018), 5-14. DOI=10.5120/ijca2018915852

@article{ 10.5120/ijca2018915852,
author = { Md. Nurul Ahad Tawhid, Emon Kumar Dey },
title = { A Gender Recognition System from Facial Image },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2018 },
volume = { 180 },
number = { 23 },
month = { Feb },
year = { 2018 },
issn = { 0975-8887 },
pages = { 5-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume180/number23/29070-2018915852/ },
doi = { 10.5120/ijca2018915852 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:01:31.546166+05:30
%A Md. Nurul Ahad Tawhid
%A Emon Kumar Dey
%T A Gender Recognition System from Facial Image
%J International Journal of Computer Applications
%@ 0975-8887
%V 180
%N 23
%P 5-14
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Automatic gender classification from facial image has become an attractive research area in the field of machine learning. Various methods have already been proposed for gender recognition in both controlled and uncontrolled situations. Problem arises in uncontrolled situation when there are high rate of noises, lack of illumination etc. To mitigate the problems, we have proposed a framework where we applied a pre-processing to enhance the images using Bilateral Histogram Equalization (BHEP) algorithm and applied the proposed framework in LFW, Adience and color FERET dataset yielding 94.29%, 84.86% and 98.30% accuracies. Confusion matrix, Precision, Recall, F-measure, True Positive Rate (TPR), True Negative Rate (TNR) etc. also shows that our proposed method performs better than the existing state of the arts.

References
  1. Timo Ahonen, Abdenour Hadid, and Matti Pietikainen. Face description with local binary patterns: Application to face recognition. IEEE transactions on pattern analysis and machine intelligence, 28:2037–2041, 2006.
  2. Feroz Mahmud Amil, Md Mostafijur Rahman, Shanto Rahman, Emon Kumar Dey, and Mohammad Shoyaib. Bilateral histogram equalization with pre-processing for contrast enhancement. In Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), 2016 17th IEEE/ACIS International Conference on, pages 231–236. IEEE, 2016.
  3. Ramin Azarmehr, Robert Laganiere, Won-Sook Lee, Christina Xu, and Daniel Laroche. Real-time embedded age and gender classification in unconstrained video. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 57–65, 2015.
  4. Shumeet Baluja and Henry A Rowley. Boosting sex identification performance. International Journal of computer vision, 71(1):111–119, 2007.
  5. Emon Kumar Dey, Md Nurul Ahad Tawhid, and Mohammad Shoyaib. An automated system for garment texture design class identification. Computers, 4:265–282, 2015.
  6. Eran Eidinger, Roee Enbar, and Tal Hassner. Age and gender estimation of unfiltered faces. IEEE Transactions on Information Forensics and Security, 9:2170–2179, 2014.
  7. Beatrice A Golomb, David T Lawrence, and Terrence J Sejnowski. Sexnet: A neural network identifies sex from human faces. In NIPS, volume 1, page 2, 1990.
  8. Rafael C Gonzalez, Richard E Woods, et al. Digital image processing. Prentice hall Upper Saddle River:, NJ, 2002.
  9. Zhenhua Guo, Lei Zhang, and David Zhang. A completed modeling of local binary pattern operator for texture classification. IEEE Transactions on Image Processing, 19:1657– 1663, 2010.
  10. Gary B Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller. Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical report, Technical Report 07-49, University of Massachusetts, Amherst, 2007.
  11. Shih-Chia Huang, Fan-Chieh Cheng, and Yi-Sheng Chiu. Efficient contrast enhancement using adaptive gamma correction with weighting distribution. IEEE Transactions on Image Processing, 22:1032–1041, 2013.
  12. Bongjin Jun, Inho Choi, and Daijin Kim. Local transform features and hybridization for accurate face and human detection. IEEE transactions on pattern analysis and machine intelligence, 35:1423–1436, 2013.
  13. Yeong-Taeg Kim. Contrast enhancement using brightness preserving bi-histogram equalization. IEEE transactions on Consumer Electronics, 43:1–8, 1997.
  14. Svetlana Lazebnik, Cordelia Schmid, and Jean Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), pages 2169–2178. IEEE, 2006.
  15. Gil Levi and Tal Hassner. Age and gender classification using convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 34–42, 2015.
  16. Erno Makinen and Roope Raisamo. Evaluation of gender classification methods with automatically detected and aligned faces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30:541–547, 2008.
  17. Choon Boon Ng, Yong Haur Tay, and Bok Min Goi. Visionbased human gender recognition: A survey. arXiv preprint arXiv:1204.1611, 2012.
  18. Timo Ojala, Matti Pietikainen, and Topi Maenpaa. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on pattern analysis and machine intelligence, 24:971–987, 2002.
  19. P Jonathon Phillips, Hyeonjoon Moon, Syed A Rizvi, and Patrick J Rauss. The feret evaluation methodology for facerecognition algorithms. IEEE Transactions on pattern analysis and machine intelligence, 22:1090–1104, 2000.
  20. P Jonathon Phillips, Harry Wechsler, Jeffery Huang, and Patrick J Rauss. The feret database and evaluation procedure for face-recognition algorithms. Image and vision computing, 16:295–306, 1998.
  21. M. M. Rahman, S. Rahman, M. Kamal, M. Abdullah-Al- Wadud, E. K. Dey, and M. Shoyaib. Noise adaptive binary pattern for face image analysis. In 2015 18th International Conference on Computer and Information Technology (ICCIT), pages 390–395, 2015.
  22. Md Mostafijur Rahman, Shanto Rahman, Emon Kumar Dey, and Mohammad Shoyaib. A gender recognition approach with an embedded preprocessing. International Journal of Information Technology and Computer Science (IJITCS), 7:19, 2015.
  23. Ad´in Ram´irez Rivera, Jorge Rojas Castillo, and Oksam Chae. Local directional texture pattern image descriptor. Pattern Recognition Letters, 51:94–100, 2015.
  24. Gregory Shakhnarovich, Paul A Viola, and Baback Moghaddam. A unified learning framework for real time face detection and classification. In Automatic Face and Gesture Recognition, 2002. Proceedings. Fifth IEEE International Conference on, pages 14–21. IEEE, 2002.
  25. Caifeng Shan. Learning local binary patterns for gender classification on real-world face images. Pattern Recognition Letters, 33:431–437, 2012.
  26. Karen Simonyan, Omkar M Parkhi, Andrea Vedaldi, and Andrew Zisserman. Fisher vector faces in the wild. In BMVC, page 4, 2013.
  27. Xiaoyang Tan and Bill Triggs. Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE transactions on image processing, 19:1635–1650, 2010.
  28. Yu Wang, Qian Chen, and Baeomin Zhang. Image enhancement based on equal area dualistic sub-image histogram equalization method. IEEE Transactions on Consumer Electronics, 45:68–75, 1999.
  29. JianxinWu and Jim M Rehg. Centrist: A visual descriptor for scene categorization. IEEE transactions on pattern analysis and machine intelligence, 33:1489–1501, 2011.
  30. Ramin Zabih and John Woodfill. Non-parametric local transforms for computing visual correspondence. In European conference on computer vision, pages 151–158. Springer, 1994.
  31. Baochang Zhang, Yongsheng Gao, Sanqiang Zhao, and Jianzhuang Liu. Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor. IEEE transactions on image processing, 19:533–544, 2010.
  32. Wenhao Zhang, Melvyn L Smith, Lyndon N Smith, and Abdul Farooq. Gender recognition from facial images: two or three dimensions? JOSA A, 33:333–344, 2016.
  33. Zhanpeng Zhang, Ping Luo, Chen Change Loy, and Xiaoou Tang. Facial landmark detection by deep multi-task learning. In European Conference on Computer Vision, pages 94–108. Springer, 2014.
Index Terms

Computer Science
Information Sciences

Keywords

Gender recognition image enhancement BHEP image preprocessing image enhancement feature extraction

Powered by PhDFocusTM