Skip to main content
SpringerLink
Log in

Applying Geometric Function on Sensors 3D Gait Data for Human Identification

  • Chapter
  • First Online:
Transactions on Computational Science XXVI

Part of the book series: Lecture Notes in Computer Science ((TCOMPUTATSCIE,volume 9550))

Abstract

In surveillance system, the video data has received a great deal of attention, instead of Mocap data, there has enough no work on recognizing of human through this data. Most Surveillance system monitors the behavior, activities, or other changing information in surrounding real life; usually it is used to recognize people to the purpose of security issues in society. This paper aims to propose a novel approach of human identification, which based on sensor data acquired by an optical system. Three joints of the human body, such as the hip, knee, and ankle joint have been selected by the amount of gait movement in this algorithm. By extracting suitable 3D static and dynamic joints feature from data. The Parametric Bezier Curve(PBC) technique applies on the extracted features in order to derive the strong correlation between joint movements. The curve control points are used to construct the triangles of each walking pose. After that centroid of triangle method apply on constructed triangle to compute a 3D center value. Selecting a triangle which has minimum distance between original pose triangle and recursive triangle center value. We then employed the geometric function to compute the area of each walking pose triangle(gait signature). Furthermore, we optimized the gait signature by using statistical moment on computed areas. After an accurate analysis the signature and found that is has a unique relationship among the 3D human gaits, and use this signature as to classify the human identification. The experiments demonstrated on IGS-90 and Vicon motion capture system data that is proved that proposed method is more accurate and reliable results.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Cmu graphics lab motion capture database. http://mocap.cs.cmu.edu. Accessed 10 February 2015

  2. Hero formula. http://en.wikipedia.org/wiki/Heron’s_formula. Accessed 14 February 2015

  3. Mocapdata. http://www.tcts.fpms.ac. Accessed 14 February 2013

  4. Optical motion. http://en.wikipedia.org/wiki/List_of_motion_and_gesture_file_formats. Accessed 10 February 2015

  5. Abson, K., Palmer, I.: Motion capture: capturing interaction between human and animal. Vis. Comput. 31(3), 1–13 (2014)

    Google Scholar 

  6. Ahn, Y.J., Kim, H.O., Lee, K.Y.: G 1 arc spline approximation of quadratic bézier curves. Comput.-Aided Des. 30(8), 615–620 (1998)

    Article  MATH  Google Scholar 

  7. Ali, S., Mingquan, Z., Zhongke, W., Razzaq, A., Hamada, M., Ahmed, H.: Comprehensive use of hip joint in gender identification using 3-dimension data. TELKOMNIKA Indonesian J. Electr. Eng. 11(6), 2933–2941 (2013)

    Google Scholar 

  8. Ali, S., Wu, Z., Zhou, M., Razzaq, A., Ahamd, H.: Human identification based on gait joints area through straight walking view. In: International Proceedings of Chemical, Biological & Environmental Engineering, vol. 56 (2013)

    Google Scholar 

  9. Anthony, D.: Statistics for Health, Life and Social Sciences. BookBoon, London (2011)

    Google Scholar 

  10. Bennett, J., Carter, C.P.: Adopting virtual production for animated filmaking (2014)

    Google Scholar 

  11. Chai, Y., Ren, J., Zhao, R., Jia, J.: Automatic gait recognition using dynamic variance features. In: 7th International Conference on Automatic Face and Gesture Recognition, FGR 2006, pp. 475–480. IEEE (2006)

    Google Scholar 

  12. Chen, C., Liang, J., Zhao, H., Hu, H., Tian, J.: Frame difference energy image for gait recognition with incomplete silhouettes. Pattern Recogn. Lett. 30(11), 977–984 (2009)

    Article  Google Scholar 

  13. Etemad, S.A., Arya, A.: Extracting movement, posture, and temporal style features from human motion. Biologically Inspired Cogn. Architectures 7, 15–25 (2014)

    Article  Google Scholar 

  14. Fatima, I., Fahim, M., Lee, Y.K., Lee, S.: A unified framework for activity recognition-based behavior analysis and action prediction in smart homes. Sensors 13(2), 2682–2699 (2013)

    Article  Google Scholar 

  15. Feng, X., Qu, S., Wu, L.: Foot trajectory kept motion retargeting. In: 2011 International Conference on Virtual Reality and Visualization (ICVRV), pp. 247–250. IEEE (2011)

    Google Scholar 

  16. Golomb, B.A., Lawrence, D.T., Sejnowski, T.J.: Sexnet: a neural network identifies sex from human faces. In: NIPS, pp. 572–579 (1990)

    Google Scholar 

  17. Grewal, B.S., Grewal, J.: Higher Engineering Mathematics, vol. 8. Khanna Publishers, Delhi (2005)

    Google Scholar 

  18. Han, J., Bhanu, B.: Individual recognition using gait energy image. IEEE Trans. Pattern Anal. Mach. Intell. 28(2), 316–322 (2006)

    Article  Google Scholar 

  19. Hanmandlu, M., Gupta, R.B., Sayeed, F., Ansari, A.: An experimental study of different features for face recognition. In: 2011 International Conference on Communication Systems and Network Technologies (CSNT), pp. 567–571. IEEE (2011)

    Google Scholar 

  20. Hayfron-Acquah, J.B., Nixon, M.S., Carter, J.N.: Automatic gait recognition by symmetry analysis. Pattern Recogn. Lett. 24(13), 2175–2183 (2003)

    Article  Google Scholar 

  21. Hill, F., Kelley, S.: Computer Graphics Using OpenGL, 3/E. Pearson, Upper Saddle River (2007)

    Google Scholar 

  22. Hoyet, L., Ryall, K., Zibrek, K., Park, H., Lee, J., Hodgins, J., O’Sullivan, C.: Evaluating the distinctiveness and attractiveness of human motions on realistic virtual bodies. ACM Trans. Graph. (TOG) 32(6), 204 (2013)

    Article  Google Scholar 

  23. Hsieh, M.K., Chen, B.Y., Ouhyoung, M.: Motion retargeting and transition in different articulated figures. In: Ninth International Conference on Computer Aided Design and Computer Graphics, p. 6. IEEE (2005)

    Google Scholar 

  24. Hu, M., Wang, Y.: A new approach for gender classification based on gait analysis. In: Fifth International Conference on Image and Graphics, ICIG 2009, pp. 869–874. IEEE (2009)

    Google Scholar 

  25. Huang, X., Boulgouris, N.V.: Gait recognition using multiple views. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2008, pp. 1705–1708. IEEE (2008)

    Google Scholar 

  26. Huang, X., Boulgouris, N.V.: Gait recognition for randomwalking patterns and variable body postures. In: 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 1726–1729. IEEE (2010)

    Google Scholar 

  27. Jain, A., Huang, J.: Integrating independent components and linear discriminant analysis for gender classification. In: Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 159–163. IEEE (2004)

    Google Scholar 

  28. Lu, W., Liu, Y., Sun, J., Sun, L.: A motion retargeting method for topologically different characters. In: Sixth International Conference on Computer Graphics, Imaging and Visualization, CGIV 2009, pp. 96–100. IEEE (2009)

    Google Scholar 

  29. McCormick, J., Nash, A., Hutchison, S., Vincs, K., Nahavandi, S., Creighton, D.: Recognition: combining human interaction and a digital performing agent. In: Proceedings of the 2014 Virtual Reality International Conference, p. 19. ACM (2014)

    Google Scholar 

  30. Meredith, M., Maddock, S.: Motion capture file formats explained. Dept. Comput. Sci. Univ. Sheffield 211, 241–244 (2001)

    Google Scholar 

  31. Moghaddam, B., Yang, M.H.: Learning gender with support faces. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 707–711 (2002)

    Article  Google Scholar 

  32. Nixon, M.S., Carter, J.N.: Automatic recognition by gait. Proc. IEEE 94(11), 2013–2024 (2006)

    Article  Google Scholar 

  33. Shutler, J.D., Nixon, M.S., Harris, C.J.: Statistical gait description via temporal moments. In: Proceedings of the 4th IEEE Southwest Symposium Image Analysis and Interpretation, pp. 291–295. IEEE (2000)

    Google Scholar 

  34. Tilmanne, J., Sebbe, R., Dutoit, T.: A database for stylistic human gait modeling and synthesis. In: Proceedings of the eNTERFACE 2008 Workshop on Multimodal Interfaces, pp. 91–94. Citeseer, Paris (2008)

    Google Scholar 

  35. Wallraven, C., Schultze, M., Mohler, B., Volkova, E., Alexandrova, I., Vatakis, A., Pastra, K., Spence, C., Navarra, J., Vatakis, A., et al.: Understanding objects and actions-a vr experiment. Perception 38, 113 (2014)

    Google Scholar 

  36. Wang, A.H., Liu, J.W.: A gait recognition method based on positioning human body joints. In: International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2007, vol. 3, pp. 1067–1071. IEEE (2007)

    Google Scholar 

  37. Wang, J., She, M., Nahavandi, S., Kouzani, A.: A review of vision-based gait recognition methods for human identification. In: 2010 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 320–327. IEEE (2010)

    Google Scholar 

  38. Weimin, X., Ying, L., Hongzhe, H., Lun, X., ZhiLiang, W., et al.: New approach of gait recognition for human id. In: Proceedings of the 2004 7th International Conference on Signal Processing, ICSP 2004, vol. 1, pp. 199–202. IEEE (2004)

    Google Scholar 

  39. Xiao, Q.: Technology review-biometrics-technology, application, challenge, and computational intelligence solutions. IEEE Comput. Intell. Mag. 2(2), 5–25 (2007)

    Article  Google Scholar 

  40. Xu, D., Yan, S., Tao, D., Zhang, L., Li, X., Zhang, H.J.: Human gait recognition with matrix representation. IEEE Trans. Circuits and Syst. Video Technol. 16(7), 896–903 (2006)

    Article  Google Scholar 

  41. Xu, X., Leng, C., Wu, Z.: Rapid 3d human modeling and animation based on sketch and motion database. In: 2011 Workshop on Digital Media and Digital Content Management (DMDCM), pp. 121–124. IEEE (2011)

    Google Scholar 

  42. Yih, E.W.K., Sainarayanan, G., Chekima, A.: Palmprint based biometric system: a comparative study on discrete cosine transform energy, wavelet transform energy and sobelcode methods. Biomed. Soft Comput. Hum. Sci. 14(1), 11–19 (2009)

    Google Scholar 

  43. Yoo, I., Vanek, J., Nizovtseva, M., Adamo-Villani, N., Benes, B.: Sketching human character animations by composing sequences from large motion database. Visual Comput. 30(2), 213–227 (2014)

    Article  Google Scholar 

  44. Yu, S., Tan, T., Huang, K., Jia, K., Wu, X.: A study on gait-based gender classification. IEEE Trans. Image Process. 18(8), 1905–1910 (2009)

    Article  MathSciNet  Google Scholar 

  45. Zhang, B., Wei, D., Yan, K., Jiang, S.: Human Walking Analysis Evaluation and Classification Based on Motion Capture System. INTECH Open Access Publisher, Winchester (2011)

    Book  Google Scholar 

  46. Zhang, M.Y.: Application of performance motion capture technology in film and television performance animation. Appl. Mechan. Mater. 347, 2781–2784 (2013)

    Article  Google Scholar 

  47. Zordan, V.B., Van Der Horst, N.C.: Mapping optical motion capture data to skeletal motion using a physical model. In: Proceedings of the 2003 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pp. 245–250. Eurographics Association (2003)

    Google Scholar 

Download references

Acknowledgments

The data used in this paper were obtained at [3]. We thank them for sharing with us. This work has supported by the National Natural Science Foundation of China under Grant No.61170203, 61170170, The National Key Technology Research and Development Program of China under Grant No.2012BAH33F04, Beijing Key Laboratory Program of China under Grant No.Z111101055281056.

Author information

Authors and Affiliations

  1. College of Information Science and Technology, Engineering Research Center of Institute Virtual Reality and Visualization Technology, Beijing Normal University, Beijing, China

    Sajid Ali, Zhongke Wu, Nighat Saeed & Mingquan Zhou

  2. MOE Key Laboratory of Space Robotics, Beijing University of Posts and Telecommunications, Beijing, China

    Xulong Li

  3. Robotics Institute, Carnegie Mellon University, Pittsburgh, USA

    Xulong Li

  4. School of Information Science and Technology, Northwest University, Xian, China

    Dong Wang

Authors
  1. Sajid Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhongke Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xulong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nighat Saeed
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mingquan Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sajid Ali .

Editor information

Editors and Affiliations

  1. University of Calgary, Calgary, Alberta, Canada

    Marina L. Gavrilova

  2. Sardina Systems OÜ, Tallinn, Estonia

    C.J. Kenneth Tan

  3. University of Cantabria, Santander, Spain

    Andrés Iglesias

  4. Toho University, Tokyo, Japan

    Mikio Shinya

  5. University of Cantabria, Santander, Spain

    Akemi Galvez

  6. Nanyang Technological University, Singapore, Singapore

    Alexei Sourin

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Ali, S., Wu, Z., Li, X., Saeed, N., Wang, D., Zhou, M. (2016). Applying Geometric Function on Sensors 3D Gait Data for Human Identification. In: Gavrilova, M., Tan, C., Iglesias, A., Shinya, M., Galvez, A., Sourin, A. (eds) Transactions on Computational Science XXVI. Lecture Notes in Computer Science(), vol 9550. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49247-5_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-49247-5_8

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-49246-8

  • Online ISBN: 978-3-662-49247-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation