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Applied Intelligence

, Volume 48, Issue 5, pp 1200–1217 | Cite as

A view-invariant gait recognition algorithm based on a joint-direct linear discriminant analysis

  • Jose Portillo-Portillo
  • Roberto Leyva
  • Victor Sanchez
  • Gabriel Sanchez-Perez
  • Hector Perez-Meana
  • Jesus Olivares-Mercado
  • Karina Toscano-Medina
  • Mariko Nakano-Miyatake
Article
First Online:

Abstract

This paper proposes a view-invariant gait recognition algorithm, which builds a unique view invariant model taking advantage of the dimensionality reduction provided by the Direct Linear Discriminant Analysis (DLDA). Proposed scheme is able to reduce the under-sampling problem (USP) that appears usually when the number of training samples is much smaller than the dimension of the feature space. Proposed approach uses the Gait Energy Images (GEIs) and DLDA to create a view invariant model that is able to determine with high accuracy the identity of the person under analysis independently of incoming angles. Evaluation results show that the proposed scheme provides a recognition performance quite independent of the view angles and higher accuracy compared with other previously proposed gait recognition methods, in terms of computational complexity and recognition accuracy.

Keywords

Gait recognition scheme View-invariant method Gait energy image Direct linear discriminant analysis (DLDA) Euclidean distance 
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Notes

Acknowledgements

The authors thank the National Science and Technology Council of Mexico (CONACyT), the Instituto Politécnico Nacional of Mexico and the University of Warwick of the United Kingdom for the financial support during the realization of this research.

References

  1. 1.
    Bashir K, Xiang T, Gong S (2010) Gait recognition without subject cooperation. Pattern Recogn Lett 31(13):2052–2060CrossRefGoogle Scholar
  2. 2.
    Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720CrossRefGoogle Scholar
  3. 3.
    Benitez-Garcia G, Olivares-Mercado J, Sanchez-Perez G, Nakano-Miyatake M, Perez-Meana H (2013) A sub-block-based eigenphases algorithm with optimum sub-block size. Knowl-Based Syst 37:415–426CrossRefGoogle Scholar
  4. 4.
    Bodor R, Drenner A, Fehr D, Masoud O, Papanikolopoulos N (2009) View-independent human motion classification using image-based reconstruction. Image Vis Comput 27(8):1194–1206. http://www.sciencedirect.com/science/article/pii/S0262885608002412 CrossRefGoogle Scholar
  5. 5.
    Chapelle O, Keerthi SS (2010) Efficient algorithms for ranking with svms. Inf Retr 13(3):201–215CrossRefGoogle Scholar
  6. 6.
    Chaudhari JP, Dixit VV, Patil PM, Kosta YP (2015) Multimodal biometric-information fusion using the radon transform. J Electron Imag 24(2):023,017–023,017CrossRefGoogle Scholar
  7. 7.
    Chen LF, Liao HYM, Ko MT, Lin JC, Yu GJ (2000) A new lda-based face recognition system which can solve the small sample size problem. Pattern Recogn 33(10):1713–1726. http://www.sciencedirect.com/science/article/pii/S0031320399001399
  8. 8.
    Choudhury SD, Tjahjadi T (2015) Robust view-invariant multiscale gait recognition. Pattern Recogn 48(3):798–811. http://www.sciencedirect.com/science/article/pii/S0031320314003835 CrossRefGoogle Scholar
  9. 9.
    Gu B, Sheng VS (2017) A robust regularization path algorithm for ν-support vector classification. IEEE Trans Neural Netw Learn Syst 28(5):1241–1248CrossRefGoogle Scholar
  10. 10.
    Gu B, Sheng VS, Tay KY, Romano W, Li S (2015) Incremental support vector learning for ordinal regression. IEEE Trans Neural Netw Learn Syst 26(7):1403–1416MathSciNetCrossRefGoogle Scholar
  11. 11.
    Hamouchene I, Aouat S (2016) Efficient approach for iris recognition. SIViP, pp 1–7Google Scholar
  12. 12.
    Han J, Bhanu B (2006) Individual recognition using gait energy image. IEEE Trans Pattern Anal Mach Intell 28(2):316–322CrossRefGoogle Scholar
  13. 13.
    Hu M, Wang Y, Zhang Z, Little JJ, Huang D (2013) View-invariant discriminative projection for multi-view gait-based human identification. IEEE Trans Inf Forens Secur 8(12):2034–2045CrossRefGoogle Scholar
  14. 14.
    Jean F, Bergevin R, Albu AB (2009) Computing and evaluating view-normalized body part trajectories. Image Vis Comput 27(9):1272–1284. http://www.sciencedirect.com/science/article/pii/S0262885608002497
  15. 15.
    Jimenez LO, Landgrebe DA (1998) Supervised classification in high-dimensional space: geometrical, statistical, and asymptotical properties of multivariate data. IEEE Trans Syst Man Cybern Part C (Appl Rev) 28(1):39–54CrossRefGoogle Scholar
  16. 16.
    Kale A, Chowdhury A, Chellappa R (2003) Towards a view invariant gait recognition algorithm. In: Proceedings of the IEEE conference on advanced video and signal based surveillance, 2003, pp 143–150. doi: 10.1109/AVSS.2003.1217914
  17. 17.
    Kusakunniran W, Wu Q, Li H, Zhang J (2009) Multiple views gait recognition using view transformation model based on optimized gait energy image. In: 2009 IEEE 12th international conference on computer vision workshops (ICCV Workshops). IEEE, pp 1058–1064Google Scholar
  18. 18.
    Kusakunniran W, Wu Q, Zhang J, Li H (2010) Multi-view gait recognition based on motion regression using multilayer perceptron. In: 2010 20th International conference on pattern recognition (ICPR). IEEE, pp 2186–2189Google Scholar
  19. 19.
    Kusakunniran W, Wu Q, Zhang J, Li H (2010) Support vector regression for multi-view gait recognition based on local motion feature selection. In: 2010 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 974–981Google Scholar
  20. 20.
    Kusakunniran W, Wu Q, Zhang J, Li H (2012) Gait recognition under various viewing angles based on correlated motion regression. IEEE Trans Circ Syst Vid Technol 22(6):966–980CrossRefGoogle Scholar
  21. 21.
    Lee WO, Kim YG, Hong HG, Park KR (2014) Face recognition system for set-top box-based intelligent tv. Sensors 14(11):21,726–21,749CrossRefGoogle Scholar
  22. 22.
    Liu N, Tan YP (2010) View invariant gait recognition. In: 2010 IEEE international conference on acoustics speech and signal processing (ICASSP), pp 1410–1413. doi: 10.1109/ICASSP.2010.5495466
  23. 23.
    Liu N, Lu J, Tan YP (2011) Joint subspace learning for view-invariant gait recognition. Signal Process Lett IEEE 18(7):431–434CrossRefGoogle Scholar
  24. 24.
    Lu J, Tan YP (2010) Uncorrelated discriminant simplex analysis for view-invariant gait signal computing. Pattern Recogn Lett 31(5):382–393CrossRefGoogle Scholar
  25. 25.
    Lv Z, Xing X, Wang K, Guan D (2015) Class energy image analysis for video sensor-based gait recognition: a review. Sensors 15(1):932–964CrossRefGoogle Scholar
  26. 26.
    Makihara Y, Sagawa R, Mukaigawa Y, Echigo T, Yagi Y (2006) Gait recognition using a view transformation model in the frequency domain. In: Computer vision–ECCV 2006. Springer, pp 151–163Google Scholar
  27. 27.
    Mansur A, Makihara Y, Muramatsu D, Yagi Y (2014) Cross-view gait recognition using view-dependent discriminative analysis. In: 2014 IEEE international joint conference on biometrics (IJCB), pp 1–8. doi: 10.1109/BTAS.2014.6996272
  28. 28.
    Martin-Felez R, Xiang T (2012) Gait recognition by ranking. In: Fitzgibbon A, Lazebnik S, Perona P, Sato Y, Schmid C (eds) Computer vision ECCV 2012, lecture notes in computer science, vol 7572. Springer, Berlin, pp 328–341Google Scholar
  29. 29.
    Muramatsu D, Shiraishi A, Makihara Y, Yagi Y (2012) Arbitrary view transformation model for gait person authentication. In: 2012 IEEE 5th international conference on biometrics: theory, applications and systems (BTAS). IEEE, pp 85–90Google Scholar
  30. 30.
    Ng CB, Tay YH, Goi BM (2015) A review of facial gender recognition. Pattern Anal Applic 18(4):739–755MathSciNetCrossRefGoogle Scholar
  31. 31.
    Nixon MS, Tan T, Chellappa R (2010) Human identification based on gait, vol 4. Springer Science & Business MediaGoogle Scholar
  32. 32.
    Pan Z, Zhang Y, Kwong S (2015) Efficient motion and disparity estimation optimization for low complexity multiview video coding. IEEE Trans Broadcast 61(2):166–176CrossRefGoogle Scholar
  33. 33.
    Portillo J, Leyva R, Sanchez V, Sanchez G, Perez-Meana H, Olivares J, Toscano K, Nakano M (2016) View-invariant gait recognition using a joint-dlda framework. In: International conference on industrial, engineering and other applications of applied intelligent systems. Springer, pp 398–408Google Scholar
  34. 34.
    Qin AK, Shi S, Suganthan PN, Loog M (2005) Enhanced direct linear discriminant analysis for feature extraction on high dimensional data. In: Proceedings of the national conference on artificial intelligence, vol 20. AAAI Press; MIT Press, Menlo Park, CA; Cambridge, MA; London, p 851Google Scholar
  35. 35.
    Song H, Brandt-Pearce M (2012) A 2-d discrete-time model of physical impairments in wavelength-division multiplexing systems. J Lightwave Technol 30(5):713–726CrossRefGoogle Scholar
  36. 36.
    Song H, Brandt-Pearce M (2013) Range of influence and impact of physical impairments in long-haul dwdm systems. J Lightwave Technol 31(6):846–854CrossRefGoogle Scholar
  37. 37.
    Sugiyama M (2007) Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis. J Mach Learn Res 8:1027–1061. http://dl.acm.org/citation.cfm?id=1248659.1248694 zbMATHGoogle Scholar
  38. 38.
    Tao D, Li X, Wu X, Maybank S (2007) General tensor discriminant analysis and gabor features for gait recognition. IEEE Trans Pattern Anal Mach Intell 29(10):1700–1715. doi: 10.1109/TPAMI.2007.1096 CrossRefGoogle Scholar
  39. 39.
    Wang J, She M, Nahavandi S, Kouzani A (2010) A review of vision-based gait recognition methods for human identification. In: 2010 international conference on digital image computing: techniques and applications (DICTA). IEEE, pp 320–327Google Scholar
  40. 40.
    Wei W, Qi Y (2011) Information potential fields navigation in wireless ad-hoc sensor networks. Sensors 11(5):4794–4807CrossRefGoogle Scholar
  41. 41.
    Wei W, Yang XL, Shen PY, Zhou B (2012) Holes detection in anisotropic sensornets: topological methods. Int J Distrib Sensor NetwGoogle Scholar
  42. 42.
    Yu H, Yang J (2001) A direct {LDA} algorithm for high-dimensional data with application to face recognition. Pattern Recog 34(10):2067–2070. http://www.sciencedirect.com/science/article/pii/S003132030000162X CrossRefzbMATHGoogle Scholar
  43. 43.
    Yu S, Tan D, Tan T (2006) A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In: 18th international conference on pattern recognition, 2006. ICPR 2006, vol 4, pp 441–444Google Scholar
  44. 44.
    Zhang L, Qiao L, Chen S (2010) Graph-optimized locality preserving projections. Pattern Recogn 43(6):1993–2002CrossRefzbMATHGoogle Scholar
  45. 45.
    Zhang Z, Troje NF (2005) View-independent person identification from human gait. Neurocomputing 69(13):250–256. http://www.sciencedirect.com/science/article/pii/S0925231205001797, neural Networks in Signal Processing 2003 {IEEE} International Workshop on Neural Networks for Signal ProcessingCrossRefGoogle Scholar
  46. 46.
    Zhao G, Liu G, Li H, Pietikainen M (2006) 3d gait recognition using multiple cameras. In: 7th international conference on automatic face and gesture recognition, 2006. FGR 2006, pp 529–534Google Scholar
  47. 47.
    Zheng S, Zhang J, Huang K, He R, Tan T (2011) Robust view transformation model for gait recognition. In: 2011 18th IEEE international conference on image processing. IEEE, pp 2073–2076Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Jose Portillo-Portillo
    • 1
  • Roberto Leyva
    • 2
  • Victor Sanchez
    • 2
  • Gabriel Sanchez-Perez
    • 1
  • Hector Perez-Meana
    • 1
  • Jesus Olivares-Mercado
    • 1
  • Karina Toscano-Medina
    • 1
  • Mariko Nakano-Miyatake
    • 1
  1. 1.Instituto Politécnico NacionalESIME CulhuacanCoyoacanMexico
  2. 2.Department of Computer ScienceUniversity of WarwickCoventryUK

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