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Gender Classification Based on Deep Learning

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Big Data and Visual Analytics

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  1. 1.

    The original datasets are available at https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/.

References

  1. Lu, H., Lin, H.: Gender recognition using Adaboosted feature. In: Third International Conference on Natural Computation (ICNC 2007) (2007)

    Google Scholar 

  2. Deng, Q., Xu, Y., Wang, J., Sun, K.: Deep learning for gender recognition. In: 2015 International Conference on Computers, Communications, and Systems (ICCCS), pp. 206–209 (2015) [Online]. Available http://ieeexplore.ieee.org/document/7562902/

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

    Google Scholar 

  4. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) 2001, vol. 1, pp. I-511-I-518 (2001). https://doi.org/10.1109/CVPRW.2015.7301352

  5. Ng, C., Tay, Y., Goi, B.M.: Recognizing human gender in computer vision: a survey. In: PRICAI 2012: Trends in Artificial Intelligence. Lecture Notes in Computer Science, vol. 7458, pp. 335–346 (2012)

    Article  Google Scholar 

  6. Moghaddam, B., Yang, M.H.: Learning gender with support faces. IEEE Trans. Patt. Anal. Mach. Intell. 24(5), 707–711 (2002)

    Article  Google Scholar 

  7. Verschae, R., Solar, J., Correa, M.: Gender classification of faces using Adaboost. In: Progress in Pattern Recognition, Image Analysis and Applications, pp. 68–78 (2006)

    Google Scholar 

  8. Lin, H., Lu, H., Zhang, L.: A new automatic recognition system of gender, age and ethnicity. In: 2006 6th World Congress on Intelligent Control and Automation, vol. 3, pp. 9988–9991 (2006) [Online]. Available http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1713951

  9. Lian, H.-C., Lu, B.-L.: Multi-view gender classification using multi-resolution local binary patterns and support vector machines. Int. J. Neural Syst. 17, 479–87 (2007) [Online]. Available http://www.ncbi.nlm.nih.gov/pubmed/18186597

  10. Ardakany, A., Jou la, A.: Gender recognition based on edge histogram. Int. J. Comput. Theor. Eng. 4, 127–130 (2012)

    Article  Google Scholar 

  11. Bekhouche, S.E., Ouafi, A., Benlamoudi, A., Taleb-Ahmed, A., Hadid, A.: Facial age estimation and gender classification using multi level local phase quantization. In: 3rd International Conference on Control, Engineering and Information Technology, CEIT 2015, pp. 3–6 (2015)

    Google Scholar 

  12. Nguyen, H.T.: Combining local features for gender classification. In: Proceedings of 2015 2nd National Foundation for Science and Technology Development Conference on Information and Computer Science, NICS 2015, pp. 130–134 (2015)

    Google Scholar 

  13. Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015) [Online]. Available http://dx.doi.org/10.1016/j.neunet.2014.09.003

  14. Sun, W., Su, F.: Regularization of deep neural networks using a novel companion objective function. In: International Conference on Image Processing (ICIP), pp. 2865–2869 (2015)

    Google Scholar 

  15. Nielsen, M.: Neural networks and deep learning (2015) [Online]. Available http://neuralnetworksanddeeplearning.com/index.html

  16. Powers, D.M.W.: Evaluation: from precision, recall and F-factor to ROC, informedness, markedness & correlation. Technical Report SIE-07–001. School of Informatics and Engineering Flinders University, Adelaide (2007)

    Google Scholar 

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Correspondence to Mingon Kang .

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Gharana, D., Suh, S.C., Kang, M. (2017). Gender Classification Based on Deep Learning. In: Suh, S., Anthony, T. (eds) Big Data and Visual Analytics. Springer, Cham. https://doi.org/10.1007/978-3-319-63917-8_3

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  • DOI: https://doi.org/10.1007/978-3-319-63917-8_3

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