Transactions of the Korean Society of Mechanical Engineers A (대한기계학회논문집A)

The Korean Society of Mechanical Engineers (대한기계학회)
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Method for Classification of Age and Gender Using Gait Recognition

걸음걸이 인식을 통한 연령 및 성별 분류 방법

  • Yoo, Hyun Woo (Engineering Research Center, MBC) ;
  • Kwon, Ki Youn (School of Industrial Engineering, Kumoh National Institute of Technology)
  • Received : 2017.03.15
  • Accepted : 2017.08.24
  • Published : 2017.11.01
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Abstract

Classification of age and gender has been carried out through different approaches such as facial-based and audio-based classifications. One of the limitations of facial-based methods is the reduced recognition rate over large distances, while another is the prerequisite of the faces to be located in front of the camera. Similarly, in audio-based methods, the recognition rate is reduced in a noisy environment. In contrast, gait-based methods are only required that a target person is in the camera. In previous works, the view point of a camera is only available as a side view and gait data sets consist of a standard gait, which is different from an ordinary gait in a real environment. We propose a feature extraction method using skeleton models from an RGB-D sensor by considering characteristics of age and gender using ordinary gait. Experimental results show that the proposed method could efficiently classify age and gender within a target group of individuals in real-life environments.

얼굴 모양 및 목소리를 이용하는 방법을 포함하여 연령 및 성별을 분류하는 다양한 방법이 연구되고 있다. 그러나 얼굴 기반 방법은 원거리에서 인식률이 급격히 감소하고, 오디오 기반 방법은 잡음이 많은 환경에서는 적용하기 어렵다. 대조적으로 보행 기반 방법은 대상자가 카메라에 촬영만 되면 인식이 가능하다. 기존 연구에서 카메라의 시점은 측면에서만 볼 수 있어서 실제 환경에서 일반 보행과는 현실적으로 차이가 발생했다. 본 연구에서는 일반 보행 데이터를 이용하여 연령과 성별을 분류할 수 있도록 RGB-D 센서로부터 획득된 골격 모델을 이용한 특징 추출 방법을 제안한다. 실험 결과는 제안된 방법이 실제 환경에서 효율적임을 보여준다.

Keywords

References

  1. Lee, E. A. and Kim, M. S., 2000, "A Study of Clothing Purchase Behaviors According to Subjective Age," Journal of the Korean Society of Clothing and Textiles, Vol. 24, No. 8, pp. 1254-1265.
  2. Kim, S. H. and Han, G. T., 2016, "A Facial Feature Area Extraction Method for Improving Face Recognition Rate in Camera Image," KIPS Transactions on Software and Data, Vol. 23, No. 5, pp. 251- 260.
  3. Lee, B. G., Choi, J. S., Yoon, S. S., Choi, M. T., Kim, M. S. and Kim, D. J., 2011, "Audio-Visual Fusion for Sound Source Localization and Improved Attention," Trans. Korean Soc. Mech. Eng. A, Vol. 35, No. 7, pp. 737-743. https://doi.org/10.3795/KSME-A.2011.35.7.737
  4. Nixon, M. S. and Carter, J. N., 2006, "Automatic Recognition by Gait," Proc. IEEE, Vol. 94, No. 11, pp. 2013-2024. https://doi.org/10.1109/JPROC.2006.886018
  5. Boulgouris, N. V., Hatzinakos, D. and Plataniotis, K. N., 2005, "Gait Recognition: a Challenging Signal Processing Technology for Biometric Identification," IEEE Signal Processing Magazine, Vol. 22, No. 6, pp. 78-90. https://doi.org/10.1109/MSP.2005.1550191
  6. Sarkar, S., Phillips, P. J., Liu, Z., Grother, I. R., Vega, P. and Bowyer, K. W., 2005, "The Human ID Gait Challenge Problem: Data Sets, Performance, and Analysis," IEEE Trans. Pattern Anal. Mach. Intell., Vol. 27, No. 2, pp. 162-177. https://doi.org/10.1109/TPAMI.2005.39
  7. Hayfron-Acquah, J. B., Nixon, M. S. and Carter, J. N., 2002, "Human Identification by Spatio-Temporal Symmetry. In Pattern Recognition," Proceedings. 16th International Conference, Vol. No. 1, pp. 632-635.
  8. Han, J. and Bhanu, B., 2006, "Individual Recognition using Gait Energy Image," IEEE Trans. Pattern Anal. Mach. Intell., Vol. 28, No. 2, pp. 316-322. https://doi.org/10.1109/TPAMI.2006.38
  9. Bobick, A. F. and Johnson, A. Y., 2001, "Gait Recognition using Static, Activity-Specific Parameters," The 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 1, pp. 423-430.
  10. Lee, L. and Grimson, W. E. L., 2002, "Gait Analysis for Recognition and Classification," Proceedings. Fifth IEEE International Conference, pp. 155-162.
  11. Yoo, J. H., Hwang, D., Moon, K. Y. and Nixon, M. S., 2008, "Automated Human Recognition by Gait using Neural Network," In Image Processing Theory, Tools and Applications, IPTA 2008, pp. 1-6.
  12. http://www.microsoft.com/en-us/kinectforwindows/, Internet, 2017.
  13. Shotton, J., Sharp, T., Kipman, A., Fitzgibbon, A., Finocchio, M., Blake, A. and Moore, R., 2011, "Real- Time Human Pose Recognition in Parts from Single Depth Images," IEEE Conference on Computer Vision and Pattern Recognition, pp. 1297-1304.
  14. Yu, S., Tan, D. and Tan, T., 2006, "A framework for Evaluating the Effect of View Angle, Clothing and Carrying Condition on Gait Recognition," In Proc. 18th Int. Conf. Pattern Recognition, pp. 441-444.
  15. Zhang, D., Wang, Y. and Bhanu, B., 2010, "Age Classification Base on Gait using HMM," In Pattern Recognition, 20th International Conference, pp. 3834-3837.
  16. Yu, S., Tan, T., Huang, K., Jia, K. and Wu, X., 2009, "A Study on Gait-Based Gender Classification," IEEE Transactions on Image Processing, Vol. 18, No. 8, pp. 1905-1910. https://doi.org/10.1109/TIP.2009.2020535
  17. Arai, K. and Andrie, R., 2013, "Gender Classification with Human Gait Based on Skeleton Model," 10th International Conference on Information Technology: New Generations, pp. 113-118.
  18. Sabir, A., Al-Jawad, N., Jassim, S. and Al-Talabani, A., 2013, "Human Gait Gender Classification Based on Fusing Spatio-Temporal and Wavelet Statistical Features," In Computer Science and Electronic Engineering Conference, pp. 140-145.
  19. Yoon, N. M., Yoon, H. J., Park, J. S., Jeong, H. S. and Kim, G., 2010, "The Comparative Study on Age- Associated Gait Analysis in Normal Korean," The Journal of Korean Physical Therapy, Vol. 22, No. 2, pp. 15-23.
  20. Cho, S. H., Park, J. M. and Kwon, O. Y., 2004, "Gender Differences in Three Dimensional Gait Analysis Data from 98 Healthy Korean Adults," Clinical Biomechanics, Vol. 19, No. 2, pp. 145-152. https://doi.org/10.1016/j.clinbiomech.2003.10.003
  21. He, H. and Garcia, E. A., 2009, "Learning from Imbalanced Data," IEEE Transactions on Knowledge and Data Engineering, Vol. 21, No. 9, pp. 1263-1284. https://doi.org/10.1109/TKDE.2008.239
  22. Oh, I. S., 2008, "Pattern Recognition," Kyobo book, pp. 76-78, pp. 137-170.