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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2.	王科俊,侯本博.步態識別綜述[J].中國圖象圖形學報,2007,12(7):1152-1160.
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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.
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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.
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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.	劉海濤.基于立體視覺的步態識別研究[D].合肥:中國科學技術大學,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.	馬華兵.基于機器學習和多視角信息融合的步態識別系統的研究[D].西安:西安理工大學,2008.
30.	紀陽陽.基于多類特征融合的步態識別算法[D].濟南:山東大學,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.步態識別綜述[J].中國圖象圖形學報,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. 張浩,劉志鏡.加權DTW距離的自動步態識別[J].中國圖象圖形學報,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. 張元元,吳曉娟,阮秋琦.基于切向角特征的統計步態識別[J].模式識別與人工智能,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. 趙永偉,張二虎,魯繼文,等.多特征和多視角信息融合的步態識別[J].中國圖象圖形學報,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. 劉海濤.基于立體視覺的步態識別研究[D].合肥:中國科學技術大學,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. 馬華兵.基于機器學習和多視角信息融合的步態識別系統的研究[D].西安:西安理工大學,2008.
30. 紀陽陽.基于多類特征融合的步態識別算法[D].濟南:山東大學,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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