Browse > Article
http://dx.doi.org/10.3795/KSME-A.2017.41.11.1035

Method for Classification of Age and Gender Using Gait Recognition  

Yoo, Hyun Woo (Engineering Research Center, MBC)
Kwon, Ki Youn (School of Industrial Engineering, Kumoh National Institute of Technology)
Publication Information
Transactions of the Korean Society of Mechanical Engineers A / v.41, no.11, 2017 , pp. 1035-1045 More about this Journal
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.
Keywords
Gait Recognition; Age Classification; Gender Classification; Pattern Analysis;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
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.   DOI
4 Nixon, M. S. and Carter, J. N., 2006, "Automatic Recognition by Gait," Proc. IEEE, Vol. 94, No. 11, pp. 2013-2024.   DOI
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.   DOI
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.   DOI
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.   DOI
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.   DOI
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 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.   DOI
20 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.
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.   DOI
22 Oh, I. S., 2008, "Pattern Recognition," Kyobo book, pp. 76-78, pp. 137-170.