30
Views
0
CrossRef citations to date
0
Altmetric
Articles

Facial gender recognition using Gabor-DCT feature extraction

, , , &

References

  • Janu, Neha, Pratistha Mathur, Sandeep Kumar Gupta, and Shubh Lakshmi Agrwal. “Performance analysis of frequency domain based feature extraction techniques for facial expression recognition.” In Cloud Computing, Data Science & Engineering-Confluence, 2017 7th International Conference on, pp. 591-594. IEEE, 2017.
  • Li, B., Lian, X. C., & Lu, B. L., “Gender classification by combining clothing, hair and facial component classifiers”, Neurocomputing, 76(1), 18–27, (2012). doi: 10.1016/j.neucom.2011.01.028
  • BenAbdelkader, C., & Griffin, P. A local region-based approach to gender classification from face images. In Computer vision and pattern recognition-workshops, 2005. CVPR Workshops. IEEE Computer Society Conference on (p. 52). IEEE. (2005, June).
  • Guo, G., Dyer, C. R., Fu, Y., & Huang, T. S. Is gender recognition affected by age? In Computer VisionWorkshops (ICCVWorkshops), 2009 IEEE 12th International Conference on (pp. 2032–2039). IEEE. (2009, September).
  • Gao, W., & Ai, H. Face gender classification on consumer images in a multiethnic environment. In International Conference on Biometrics (pp. 169–178). Springer Berlin Heidelberg. (2009, June).
  • Haider, K. Z., Nawaz, T., Habib, H. A., Maqsood, M., & Amin, T. U. Gender Classification of Consumer Face Images using Gabor Filters. International Journal of Computer Science and Network Security (IJCSNS), 16(2), 46. (2016).
  • Fu, X., Dai, G., Wang, C., & Zhang, L. Centralized Gabor gradient histogram for facial gender recognition. In Natural computation (ICNC), 2010 sixth international conference on (Vol. 4, pp. 2070–2074). IEEE. (2010, August).
  • Ojala, T., Pietikinen, M., & Menp, T. Gray scale and rotation invariant texture classification with local binary patterns. In European Conference on Computer Vision (pp. 404–420). Springer Berlin Heidelberg. (2000, June).
  • Smirg, Ondrej, Jan Mikulka, Marcos Faundez-Zanuy, Marco Grassi, and Jiri Mekyska. “Gender recognition using PCA and DCT of face images.” In International Work-Conference on Artificial Neural Networks, pp. 220-227. Springer, Berlin, Heidelberg, 2011.
  • Lowe, D. G. Distinctive image features from scale-invariant key points. International journal of computer vision, 60(2), 91–110. (2004). doi: 10.1023/B:VISI.0000029664.99615.94
  • Nguyen, Hieu V., Li Bai, and Linlin Shen. “Local gabor binary pattern whitened pca: A novel approach for face recognition from single image per person.” In International Conference on Biometrics, pp. 269-278. Springer, Berlin, Heidelberg, 2009.
  • Gupta, Sandeep K., Shubh Lakshmi Agrwal, Yogesh K. Meena, and Neeta Nain. “A hybrid method of feature extraction for facial expression recognition.” In Signal-Image Technology and Internet-Based Systems (SITIS), 2011 Seventh International Conference on, pp. 422-425. IEEE, 2011.
  • Rai, P., Khanna, P. A gender classification system robust to occlusion using gabor features based (2D) 2PCA, Journal of Visual Communication and Image Representation, 25, 1118–1129. (2014). doi: 10.1016/j.jvcir.2014.03.009
  • Li, Ming, and Baozong Yuan. “2D-LDA: A statistical linear discriminant analysis for image matrix.” Pattern Recognition Letters 26, no. 5 (2005): 527-532. doi: 10.1016/j.patrec.2004.09.007
  • Viola, Paul, and Michael Jones. “Rapid object detection using a boosted cascade of simple features.” In Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on, vol. 1, pp. I-I. IEEE, 2001.
  • Mehrotra, R., Namuduri, K. R., & Ranganathan, N. Gabor filter-based edge detection. Pattern recognition, 25(12), 1479–1494. (1992). doi: 10.1016/0031-3203(92)90121-X
  • Ignat, A., & Coman, M. Gender recognition with Gabor filters. In E-Health and Bioengineering Conference (EHB), 2015 (pp. 1–4). IEEE. (2015, November).
  • Ekenel, Hazim Kemal, and Rainer Stiefelhagen. “Analysis of local appearance-based face recognition: Effects of feature selection and feature normalization.” In Computer Vision and Pattern Recognition Workshop, 2006. CVPRW’06. Conference on, pp. 34-34. IEEE, 2006.
  • Phillips, P. Jonathon, Hyeonjoon Moon, Syed A. Rizvi, and Patrick J. Rauss. “The FERET evaluation methodology for face-recognition algorithms.” IEEE Transactions on pattern analysis and machine intelligence 22, no. 10 (2000): 1090-1104. doi: 10.1109/34.879790
  • Amari, Shun-ichi, and Si Wu. “Improving support vector machine classifiers by modifying kernel functions.” Neural Networks 12, no. 6 (1999): 783-789. doi: 10.1016/S0893-6080(99)00032-5
  • Lemley, Joseph, Sami Abdul-Wahid, DipayanBanik, and Razvan Andonie. “Comparison of Recent Machine Learning Techniques for Gender Recognition from Facial Images.” (2016).
  • Smirg, Ondrej, Jan Mikulka, Marcos Faundez-Zanuy, Marco Grassi, and Jiri Mekyska. “Gender recognition using PCA and DCT of face images.” Advances in Computational Intelligence (2011): 220-227. doi: 10.1007/978-3-642-21498-1_28
  • Yadav, Pooja, Amarjeet Poonia, Sandeep, and Shubh Lakshmi Agrwal. “Performance analysis of Gabor 2D PCA feature extraction for gender identification using face.” In Telecommunication and Networks (TEL-NET), 2017 2nd International Conference on, pp. 1-5. IEEE, 2017.
  • Makinen, Erno, and Roope Raisamo. “Evaluation of gender classification methods with automatically detected and aligned 2017 2nd International Conference on Telecommunication and Networks (TEL-NET 2017) faces.” IEEE Transactions on Pattern Analysis and Machine Intelligence 30, no. 3 (2008): 541-547. doi: 10.1109/TPAMI.2007.70800
  • Gupta, Sandeep K., and Neeta Nain. “Gabor Filter meanPCA Feature Extraction for Gender Recognition.” In Proceedings of 2nd International Conference on Computer Vision & Image Processing, pp. 79-88. Springer, Singapore, 2018.
  • S. Ram, S. Gupta, B. Agarwal, “Devanagri Character Recognition Model Using Deep Convolution Neural Network”, In Journal of Statistics and Management Systems, Taylor Francis, 21 (4), pages:593–599, 2018. doi: 10.1080/09720510.2018.1471264
  • S. Seth, B. Agarwal, “A hybrid deep learning model for detecting diabetic retinopathy”, In Journal of Statistics and Management Systems, Taylor Francis, 21 (4), pages: 569–574 2018. doi: 10.1080/09720510.2018.1466965

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.