Skip to main content
SpringerLink
Log in

Gender classification from face images using central difference convolutional networks

  • Regular Paper
  • Published:
International Journal of Multimedia Information Retrieval Aims and scope Submit manuscript

Abstract

Nowadays gender classification which plays a vital role in face recognition systems is one of the main matters in computer vision. It is difficult to classify the gender from facial images when dealing with unconstrained images in a cross-dataset protocol. In this work, we propose two convolutional neural networks where one of the networks used the central difference convolution layer and another network used the vanilla convolution layer. The system was trained with the Casia WebFace dataset and tested on two cross-datasets, labeled faces in the wild (LFW) and FEI dataset. It is worth mentioning that the experimental results show the power and effectiveness of the proposed method. This method obtains a classification rate of 97.79% for the LFW dataset and 99.10% for the FEI dataset.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

All data generated or analyzed during this study are included in this published article.

References

  1. SheikhFathollahi M, Razzazi F (2021) Music similarity measurement and recommendation system using convolutional neural networks. Int J Multimed Inf Retr 10(1):43–53

    Article  Google Scholar 

  2. Ng CB, Yong HT, Bok MG (2012) “Vision-based human gender recognition: a survey,” PRICAI 2012: trends in artificial intelligence. Lect Notes Comput Sci 7458:335–346

    Article  Google Scholar 

  3. Sajid A. K, Muhammad N, Sheeraz A, Naveed R, (2011) Gender classification using image processing techniques: a survey. In: International Multi Topic Conference (INMIC).

  4. Juan B, Jose MB, Luis B (2011) Revisiting linear discriminant techniques in gender recognition. IEEE Trans Pattern Anal Mach Intell (TPAMI) 33(4):858–864

    Article  Google Scholar 

  5. Antipov G, Berrani S, Dugely J (2016) Minimalistic CNN-based ensemble model for gender prediction from face images. Pattern Recognit Lett Elsevier 70:59–65

    Article  Google Scholar 

  6. Saha D, De D, Ghosh P, Sengupta S, Majumdar T (2020) An extensive analysis of human gender prophecy using computer vision. IRJET 7(5):1223–1229

    Google Scholar 

  7. Curtidor A, Baydyk T, Kussul E (2021) Analysis of random local descriptors in face recognition. Electronics 10(11):1–19

    Article  Google Scholar 

  8. Abdalali O. E, Eltarhouni I. W, Abdo A. A, Almajbri I. S, (2021) Gender classification using discrete cosine transform and multi classifiers. In:”The 7th International Conference on Engineering & MIS.

  9. Kaur K, Rai P and Khanna P, (2017) Gender classification system for half face images using multi manifold discriminant analysis. In: 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence, 2017, pp 595–598

  10. Kaya Y, Ertuğrul ÖF (2017) Gender classification from facial images using gray relational analysis with novel local binary pattern descriptors. Signal Image Video Process Springer 11:769–776

    Article  Google Scholar 

  11. Lian H, Lu B, (2006) Multi-view gender classification using local binary patterns and support vector machines. In: Proc Int Symp Neural Netw (ISNN), pp 202–209.

  12. Sun N, Zheng W, Sun C, Zou C and Zhao L, (2006) Gender classification based on boosting local binary pattern. In: Proc Int Symp Neural Netw (ISNN), pp 194–201.

  13. Shan C (2012) Learning local binary patterns for gender classification on real-world face images. Pattern Recognit Lett 33(4):431–437

    Article  Google Scholar 

  14. Levi G, Hassner T, (2015) Age and gender classification using convolutional neural networks. In: In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston pp 34–42.

  15. Golomb A, Lawrence T, Sejnowski J, (1990) Sexnet: A neural network identifies sex from human faces. In: Proceedings of the conference on Advances in Neural Information Processing Systems, Denver, USA.

  16. Levi G, Hassncer T, (2015) Age and gender classification using convolutional neural networks. In: Proc IEEE Conf Comput Vis Pattern Recognit Workshops (CVPRW), pp 34–42.

  17. Islam M, Tasnim N, Baek J (2020) Human gender classification using transfer learning via Pareto frontier CNN networks. Inventions 5(16):1–12

    Google Scholar 

  18. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, et al. (2015) Going deeper with convolutions. In: Proc IEEE Conf Comput Vis Pattern Recognit (CVPR), pp 1–9.

  19. Iandola F, Han S, Moskewicz M, Ashraf K, Dally W, Keutzer K, (2017) SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 0.5MB model size. In: Proc Int Conf Learn Represent (ICLR), pp 207–212.

  20. He K, Zhang X, Ren S and Sun J, (2016) Deep residual learning for image recognition. In: Proc IEEE Conf Comput Vis Pattern Recognit (CVPR), pp 770–778.

  21. Zhang X, Javid S, Dias J, Werghi N, (2021) Person gender classification on RGB-D data with self-joint attention. IEEE, Access, V. 9, https://doi.org/10.1109/ACCESS.2021.3135428.

  22. Masud M, Muhammad G, Alhumyani H, Alshamrani SS, Cheikhrouhou O, Ibrahim S, Hossain MS (2020) Deep learning-based intelligent face recognition in IoT-cloud environment. Comput Commun 152:215–222

    Article  Google Scholar 

  23. Hsu YC, Lin EL, Lin HC (2021) Age and gender recognition with random occluded data augmentation on facial images. Multimed Tools Appl 80(8):11631–11653

    Article  Google Scholar 

  24. Jabra B. M, Guetari R, Chetouani A, Tabia H, Khlifa N, (2020) Facial expression recognition using the bilinear pooling. In: 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory.

  25. Yu Z, Zhao C, Wang Z, Qin Y, Su Z, Li X, Zhou F, and Zhao G, (2020) Searching central difference convolutional networks for face anti-spoofing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 5295–5305.

  26. Boulkenafet Z, Komulainen J, Hadid A, (2015) Face anti-spoofing based on color texture analysis. In: In IEEE international conference on image processing (ICIP), pp 2636–2640.

  27. Paszke A, Gross S, Chintala S, Chanan G, Yang E, DeVito Z, Lin Z, Desmaison A, Antiga L, and Lerer A, (2017) Automatic differentiation in pytorch".

  28. Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Mawat S, Irving G, Isard M et al (2016) Tensorflow: a system for large-scale machine learning. OSDI 16:265–283

    Google Scholar 

  29. Kasinski A, Schmidt A (2010) The architecture and performance of the face and eyes detection system based on the Haar cascade classifiers. Pattern Anal Appl 13:197–211

    Article  MathSciNet  Google Scholar 

  30. Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, and Salakhutdinov R, (2012) Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580

  31. Zeiler M D, Ranzato M, Monga R, Mao M, Yang K, Le Q V, Hinton G E, (2013) On rectified linear units for speech processing. In: International Conference on Acoustics, Speech and Signal Processing. IEEE, pp 3517–3521

  32. Yi D, Lei Z, Liao S, Li Z, (2014) Learning face representation from scratch. Technical report, arXiv:1411.7923.

  33. Huang G, Ramesh M, Berg T and Learned-Miller E, (2007) Labelled faces in the wild: a database for studying face recognition in unconstrained environments. Univ Massachusetts Amherst, Massachusetts, Tech Rep 07–49.

  34. Thomaz C, (2012) FEI face database. 2012. [Online]. Available: https://fei.edu.br/~cet/facedatabase.html. [Accessed: 06 Apr 2019].

  35. Kingma D, Ba J (2015) Adam: a method for stochastic optimization. In: International conference on learning representations (ICLR), pp 232–235.

  36. Almabdy S, Elrefaei L (2021) Feature extraction and fusion for face recognition systems using pre-trained convolutional neural networks. Int J Comput Digit Syst 10:465–461

    Google Scholar 

  37. Rouhsedaghat M, Wang Y, Ge X, Hu S, You S, and Kuo J, (2020) Facehop: a light-weight low-resolution face gender classification method. arXiv preprint arXiv:2007.09510.

  38. Han H, Jain K, (2014) Age, gender and race estimation from unconstrained face images. In: Tech. Rep. No. MSU-CSE-14-5. East Lansing, Michigan: Department of Computer Science, Michigan State University.

  39. Casas P, Jimnez, D, Yu L, Castro L, (2011) Single- and cross- database benchmarks for gender classification under unconstrained settings. In: Computer vision workshops (iccv workshops), IEEE International Conference pp 2152–2159.

  40. Antipov G, Baccouche M, Berrani A, Dugelay L, (2016) Apparent age estimation from face images combining general and children-specialized deep learning models. In: IEEE CVPR Workshops, pp 801–809.

  41. Liao M, Gu X (2020) Face recognition approach by subspace extended sparse representation and discriminative feature learning. Neurocomput Elsevier 373:35–49

    Article  Google Scholar 

  42. Wadhera A, Agarwal M (2022) “Robust pattern for face recognition using combined weber and pentagonal-triangle graph structure pattern” Optik. Elsevier 259:168925

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Mohammadreza Sheikh Fathollahi & Rezvan Heidari

Authors
  1. Mohammadreza Sheikh Fathollahi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rezvan Heidari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammadreza Sheikh Fathollahi.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sheikh Fathollahi, M., Heidari, R. Gender classification from face images using central difference convolutional networks. Int J Multimed Info Retr 11, 695–703 (2022). https://doi.org/10.1007/s13735-022-00259-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13735-022-00259-0

Keywords

Search

Navigation