Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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CCTV Based Gender Classification Using a Convolutional Neural Networks

컨볼루션 신경망을 이용한 CCTV 영상 기반의 성별구분

  • Kang, Hyun Gon (Dept. of Electronics Eng., Graduate School, Kyungsung University) ;
  • Park, Jang Sik (Dept. of Electronics Eng., Graduate School, Kyungsung University) ;
  • Song, Jong Kwan (Dept. of Electronics Eng., Graduate School, Kyungsung University) ;
  • Yoon, Byung Woo (Dept. of Electronics Eng., Graduate School, Kyungsung University)
  • Received : 2016.04.27
  • Accepted : 2016.11.29
  • Published : 2016.12.30
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Abstract

Recently, gender classification has attracted a great deal of attention in the field of video surveillance system. It can be useful in many applications such as detecting crimes for women and business intelligence. In this paper, we proposed a method which can detect pedestrians from CCTV video and classify the gender of the detected objects. So far, many algorithms have been proposed to classify people according the their gender. This paper presents a gender classification using convolutional neural network. The detection phase is performed by AdaBoost algorithm based on Haar-like features and LBP features. Classifier and detector is trained with data-sets generated form CCTV images. The experimental results of the proposed method is male matching rate of 89.9% and the results shows 90.7% of female videos. As results of simulations, it is shown that the proposed gender classification is better than conventional classification algorithm.

Keywords

References

  1. S. Lawrence, C.L. Giles, A.C. Tsoi, and A.C. Back, "Face Recognition: A Convolutional Neural-Network Approach," IEEE Transactions on Neural Networks, Vol. 8, No.1, pp. 98-113, 1997. https://doi.org/10.1109/72.554195
  2. A. Krizhevsky, I. Sutskever, and G.E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," Proceeding of Advances in Neural Information Processing Systems, pp. 1097-1105, 2012.
  3. S.A. Khan, M. Nazir, and N. Riaz, "Optimized Features Selection for Gender Classification Using Optimization Algorithms," Turkish Journal of Electrical Engineering & Computer Sciences, Vol. 21, No. 5, pp. 1479-1494, 2013.
  4. M. Hu and Y.H. Wang, "A New Approach for Gender Classification Based on Gait Analysis," Proceeding of International Conference on Image and Graphics, pp. 869-874, 2009.
  5. J.H. Kang and S.Y. Kwak, "Loitering Detection Solution for CCTV Security System," Journal of Korea Multimedia Society, Vol. 17, No. 1, pp. 15-25, 2014. https://doi.org/10.9717/kmms.2014.17.1.015
  6. P. Viola and M. Jones, "Rapid Object Detection Using a Boosted Cascade of Simple Features," Computer Vision and Pattern Recognition, Vol. 1, pp. 511-518, 2001.
  7. S. Abney, R.E. Schapire, and Y. Singer, "Boosting Applied to Tagging and PP Attachment," Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, pp. 38-45, 1999.
  8. T. Ojala, M. Peitikainen, and T. Maenpaa, "Multiresolution Gray-scale and Rotation Invariant Texture Classification with Local Binary Patterns," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 7, pp. 971-987, 2002. https://doi.org/10.1109/TPAMI.2002.1017623
  9. Y.B. Shalom and K. Birmiwal, "Consistency and Robustness of PDAF for Target Tracking in a Cluttered Environments," Automatics, Vol. 19, No. 4, pp. 431-437, 1983. https://doi.org/10.1016/0005-1098(83)90059-6
  10. Y.B. Shalom, F. Daum, and J. Huang, "The Probabilistic Data Association Filter," IEEE Control Systems, Vol. 29, No. 6, pp. 82-100, 2009. https://doi.org/10.1109/MCS.2009.934469
  11. J. Marin, D. Vazquez, A.M. Lopez, J. Amores, B. Leibe, "Random Forests of Local Experts for Pedestrian Detection," Proceedings of the IEEE International Conference on Computer Vision, pp. 2592-2599, 2013.

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