Gender Classification System Based on Deep Learning in Low Power Embedded Board

저전력 임베디드 보드 환경에서의 딥 러닝 기반 성별인식 시스템 구현

  • 정현욱 (KAIST 전기 및 전자공학부) ;
  • 김대회 (KAIST 전기 및 전자공학부) ;
  • ;
  • 노용만 (KAIST 전기 및 전자공학과)
  • Received : 2016.07.07
  • Accepted : 2016.08.30
  • Published : 2017.01.31


While IoT (Internet of Things) industry has been spreading, it becomes very important for object to recognize user's information by itself without any control. Above all, gender (male, female) is dominant factor to analyze user's information on account of social and biological difference between male and female. However since each gender consists of diverse face feature, face-based gender classification research is still in challengeable research field. Also to apply gender classification system to IoT, size of device should be reduced and device should be operated with low power. Consequently, To port the function that can classify gender in real-world, this paper contributes two things. The first one is new gender classification algorithm based on deep learning and the second one is to implement real-time gender classification system in embedded board operated by low power. In our experiment, we measured frame per second for gender classification processing and power consumption in PC circumstance and mobile GPU circumstance. Therefore we verified that gender classification system based on deep learning works well with low power in mobile GPU circumstance comparing to in PC circumstance.


Supported by : 한국연구재단


  1. G. Levi and T. Hassner, "Age and gender classification using convolutional neural networks," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2015.
  2. C. Perez, J. Tapia, P. Estevez, and C. Held, "Gender classification from face images using mutual information and feature fusion," International Journal of Optomechatronics, Vol.6, No.1, pp.92-119, 2012.
  3. B. Moghaddam and M. H. Yang, "Learning gender with support faces," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.24, No.5, pp.707-711, 2002.
  4. S. Baluja and H. A. Rowley, "Boosting sex identification performance," International Journal of Computer Vision, Vol.71, No.1, pp.111-119, 2007.
  5. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," in Proceedings of the IEEE, Vol.86, No.11, pp.2278-2324, 1998.
  6. Y. Sun, X. Wang, and X. Tang, "Hybrid deep learning for face verification," in Proceedings of the IEEE International Conference on Computer Vision, 2013.
  7. D. H. Kim, S. T. Kim, and Y. M. Ro, "Latent feature representation with 3-D multi-view deep convolutional neural network for bilateral analysis in digital breast tomosynthesis," in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2016.
  8. G. B. Huang, M. Ramesh, T. Berg, and E. Learned_miller, "Labeled faces in the wild: A database for studying face recognition in unconstrained environments," Technical report 07-49, UMass, 2007.
  9. Y. Jia. Caffe: An open source convolutional architecture for fast feature embedding [Internet],, 2013.
  10. Gary Bradsky and Adrian Kaebler, "Viola Jones Face Detector," in Learning OpenCV: Computer Vision with the OpenCV Library. O'Reilly Media, 1st edition.
  11. Hyunwook Jeong, Dae Hoe Kim, Wisam J. Baddar, and Yong Man Ro, "Real-time Gender Classification based on Deep Learning in Embedded System," in the Proceedings of the KIPS Symposium, 2016.
  12. Wen, Yandong, Zhifeng Li, and Yu Qiao, "Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015.
  13. NVIDIA developer blog [Internet], jetsot-tk1/.

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