The Present Situation and Future Development of Research on New Algorithms of Gait Recognition with Multi-angles_Journal of Biomedical Engineering

Authors：

LIYibo ,  LIKun , JIXiaofei

College of Automation, Shenyang Aerospace University, Shenyang 110136, China;

Corresponding author：

LIKun, Email: likun565@163.com

Keywords：

biometrics; gait recognition; feature extraction; data fusion

DOI：

10.7507/1001-5515.20140039

Video：

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Gait recognition is a new technology in biometric recognition and medical treatment which has advantages such as long-distance and non-invasiveness. Depending on the differences between different people's walking postures, we can recognize individuals by characteristics extracted from the images of walking movement. A complete gait recognition process usually includes gait sequence acquisition, gait detection, feature extracting and recognition. In this paper, the commonly used methods of these four processes are introduced, and feature extraction methods are classified from different multi-angle views. And then the new algorithm of multi-view emerged in recent years is highlighted. In addition, this paper summarizes the existing difficulties of gait recognition, and looks into the future development trends of it.

Citation： LIYibo, LIKun, JIXiaofei. The Present Situation and Future Development of Research on New Algorithms of Gait Recognition with Multi-angles. Journal of Biomedical Engineering, 2014, 31(1): 205-209. doi: 10.7507/1001-5515.20140039 Copy

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23.	HU M D, WANG Y H, ZHANG Z X, et al. Multi-view multi-stance gait identification[C]//18th IEEE International Conference on Image Processing, Brussels:2011:541-544.
24.	LIU N N, LU J W, TAN Y P. Joint subspace learning for view-invariant gait recognition[J]. Signal Processing Letters,2011,18(7):431-434.
25.	刘海涛.基于立体视觉的步态识别研究[D].合肥:中国科学技术大学,2010.
26.	SIVAPALAN S, CHEN D, DENMAN S, et al. 3D ellipsoid fitting for multi-view gait recognition[C]//8th IEEE International Conference on Advanced Video and Signal-Based Surveillance, Klagenfurt, 2011:355-360.
27.	CANTON-FERRER C, CASAS J R, PARDAS M. Spatio-temporal alignment and hyperspherical radon transform for 3D gait recognition in multi-view environments[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, San Francisco:2010:116-121.
28.	SIVAPALAN S, CHEN D, DENMAN S, et al. Gait energy volumes and frontal gait recognition using depth images[C]//International Joint Conference on Biometrics, IEEE, Washington, DC:2011.
29.	马华兵.基于机器学习和多视角信息融合的步态识别系统的研究[D].西安:西安理工大学,2008.
30.	纪阳阳.基于多类特征融合的步态识别算法[D].济南:山东大学,2010.

1. NIXON M S,CARTER J N,NASH J M, et al. Automatic gait recognition[C]//IEEE Colloquium on Motion Analysis and Tracking, London:1999:1/3-1/6.
2. 王科俊,侯本博.步态识别综述[J].中国图象图形学报,2007,12(7):1152-1160.
3. YANG X, ZHOU Y, ZHANG T,et al. Gabor phase based gait recognition[J]. Electr Lett, 2008, 44(10):620-621.
4. MATOVSKI D S, NIXON M S, MAHMOODI S, et al. The effect of time on gait recognition performance[J]. IEEE Trans Inf Forensic Secur, 2012, 7(2):543-552.
5. BOULGOURIS N V, CHI Z X. Gait recognition using radon transform and linear discriminant analysis[J]. IEEE Trans Image Process, 2007, 16(3):731-740.
6. ZHENG S, ZHANG J G, HUANG K Q, et al. Robust view transformation model for gait recognition[C]//Proceedings of the 18th IEEE International Conference on Image Processing, Brussels:2011:2073-2076.
7. KALE A, SUNDARESAN A, RAJAGOPALAN A N, et al. Identification of humans using gait[J]. IEEE Trans Image Process, 2004, 13(9):1163-1173.
8. CHENG M H, HO M F, HUANG C L. Gait analysis for human identification through manifold learning and HMM[J].Pattern Recognition,2008,41(8):2541-2553.
9. 张浩,刘志镜.加权DTW距离的自动步态识别[J].中国图象图形学报,2010,15(5):830-836.
10. YEOH T W, TAN W H, NG H, et al. Improved Gait recognition with automatic body joint identification[C]//ZAMAN H B, ROBINSON P, PETROU M, et al. IVIC'11 Proceedings of the Second International Conference on Visual Informatics:Sustaining Research and Innovations--Part Ⅰ. Heidelberg:Springer-Verlag Berlin, 2011:245-256.
11. XU J H, CONG W, LI J, et al. Gait recognition based on key frame and elliptical model[C]//IEEE International Conference on Information and Automation,Harbin:2010:2483-2487.
12. 张元元,吴晓娟,阮秋琦.基于切向角特征的统计步态识别[J].模式识别与人工智能,2010,23(4):539-545.
13. HAN J, BHANU B. Statistical feature fusion for gait-based human recognition[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington DC, 2004, 2:842-847.
14. YANG X C, ZHOU Y, ZHANG T H, et al. Gait recognition based on dynamic region analysis[J]. Signal Processing, 2008, 88(9):2350-2356.
15. CHEN C H, LIANG J M, ZHAO H, et al. Frame difference energy image for gait recognition with incomplete silhouettes[J]. Pattern Recognit Lett, 2009, 30(11):977-984.
16. LEE C C, CHUANG C H, HSIEH J W, et al. Frame difference history image for gait recognition[C]//International Conference on Machine Learning and Cybernetics, Guilin, 2011:1785-1788.
17. ZHANG E H, ZHAO Y W, XIONG W. Active energy image plus 2DLPP for gait recognition[J]. Signal Processing, 2010, 90(7):2295-2302.
18. BASHIR K, XIANG T, GONG S G, et al. Gait recognition without subject cooperation[J]. Pattern Recognition Letters, 2010,31(13):2052-2060.
19. BOUCHRIKA I, GOFFREDO M, CARTER J N, et al. Covariate analysis for view-point independent gait recognition[C]//TISTARELLI M, NIXON M S. Proceedings of the Third International Conference on Advances in Biometrics. Heidelberg:Springer-Verlag Berlin, 2009:990-999.
20. BASHIR K, XIANG T, GONG S G. Cross-view gait recognition using correlation strength[C]//LABROSSE F, ZWIGGELAAR R, LIU Y H, et al. Proceedings of the British Machine Vision Conference. Aberystwyth:BMVA Press, 2010:109.1-109.11.
21. KUSAKUNNIRAN W, WU Q, ZHANG J, et al. Cross-view and multi-view gait recognitions based on view transformation model using multi-layer perceptron[J].Pattern Recognition Letters,2012,33(7):882-889.
22. 赵永伟,张二虎,鲁继文,等.多特征和多视角信息融合的步态识别[J].中国图象图形学报,2009,14(3):388-393.
23. HU M D, WANG Y H, ZHANG Z X, et al. Multi-view multi-stance gait identification[C]//18th IEEE International Conference on Image Processing, Brussels:2011:541-544.
24. LIU N N, LU J W, TAN Y P. Joint subspace learning for view-invariant gait recognition[J]. Signal Processing Letters,2011,18(7):431-434.
25. 刘海涛.基于立体视觉的步态识别研究[D].合肥:中国科学技术大学,2010.
26. SIVAPALAN S, CHEN D, DENMAN S, et al. 3D ellipsoid fitting for multi-view gait recognition[C]//8th IEEE International Conference on Advanced Video and Signal-Based Surveillance, Klagenfurt, 2011:355-360.
27. CANTON-FERRER C, CASAS J R, PARDAS M. Spatio-temporal alignment and hyperspherical radon transform for 3D gait recognition in multi-view environments[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, San Francisco:2010:116-121.
28. SIVAPALAN S, CHEN D, DENMAN S, et al. Gait energy volumes and frontal gait recognition using depth images[C]//International Joint Conference on Biometrics, IEEE, Washington, DC:2011.
29. 马华兵.基于机器学习和多视角信息融合的步态识别系统的研究[D].西安:西安理工大学,2008.
30. 纪阳阳.基于多类特征融合的步态识别算法[D].济南:山东大学,2010.

Journal of Biomedical Engineering

The Present Situation and Future Development of Research on New Algorithms of Gait Recognition with Multi-angles

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