Gait Recognition Using Normal Distance Map and Sparse Multilinear Laplacian Discriminant Analysis

  • Risil Chhatrala
  • Shailaja Patil
  • Dattatray V. Jadhav
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)


In visual surveillance applications, gait is the preferred candidate for recognition of the identity of the subject under consideration. Gait is a behavioral biometric that has a large amount of redundancy, complex pattern distribution and very large variability, when multiple covariate exist. This demands robust representation and computationally efficient statistical processing approaches for improved performance. In this paper, a robust representation approach called Normal Distance Map and multilinear statistical discriminant analysis called Sparse Multilinear Discriminant Analysis is applied for improving robustness against covariate variation and increase recognition accuracy. Normal Distance Map captures geometry and shape of silhouettes so as to make representation robust and Sparse Multilinear Discriminant Analysis obtains projection matrices to preserve discrimination.


Gait recognition Normal distance map Sparse multilinear discriminant analysis Biometrics etc 


  1. 1.
    Makihara Y, Matovski DS, Nixon MS, Carter JN, Yagi Y (2015) Gait recognition: databases, representations, and applications. Wiley Online LibraryGoogle Scholar
  2. 2.
    Sivarathinabala M, Abirami S, Baskaran R (2017) A study on security and surveillance system using gait recognition. In: Intelligent techniques in signal processing for multimedia security. Springer, Berlin, pp 227–252Google Scholar
  3. 3.
    Zhang Z, Hu M, Wang Y (2011) A survey of advances in biometric gait recognition. In: Chinese conference on biometric recognition. Springer, Berlin, pp 150–158CrossRefGoogle Scholar
  4. 4.
    Boulgouris NV, Hatzinakos D, Plataniotis KN (2005) Gait recognition: a challenging signal processing technology for biometric identification. IEEE Signal Process Mag 22(6):78–90CrossRefGoogle Scholar
  5. 5.
    Wang J, She M, Nahavandi S, Kouzani A (2010) A review of vision-based gait recognition methods for human identification. Digit Image Comput: Tech Appl pp 320–327Google Scholar
  6. 6.
    Han J, Bhanu B (2006) Individual recognition using gait energy image. IEEE Trans Pattern Anal Mach Intell 28:316–322CrossRefGoogle Scholar
  7. 7.
    Huang X, Boulgouris NV (2012) Gait recognition with shifted energy image and structural feature extraction. IEEE Trans Image Process 21:2256–2268MathSciNetCrossRefGoogle Scholar
  8. 8.
    Bashir K, Xiang T, Gong S (2009) Gait recognition using gait entropy image. In: In 3rd international conference on crime detection and protection, London, UKGoogle Scholar
  9. 9.
    Lam THW, Cheung K, Liu JN (2011) Gait flow image: a silhouette-based gait representation for human identification. Pattern Recogn 44:973–987CrossRefGoogle Scholar
  10. 10.
    Makihara Y, Sagawa R, Mukaigawa Y, Echigo T, Yagi Y (2006) Gait recognition using a view transformation model in the frequency domain. In: European conference on computer vision. Springer, Berlin, pp 151–163CrossRefGoogle Scholar
  11. 11.
    Hofmann M, Bachmann S, Rigoll G (2012) 2.5 d gait biometrics using the depth gradient histogram energy image. In: 2012 IEEE fifth international conference on biometrics: theory, applications and systems (BTAS). IEEE, New York, pp 399–403Google Scholar
  12. 12.
    El-Alfy H, Mitsugami I, Yagi Y (2014) A new gait-based identification method using local gauss maps. In: Asian conference on computer vision. Springer, Berlin, pp 3–18Google Scholar
  13. 13.
    Tang S, Wang X, Lv X, Han TX, Keller J, He Z, Skubic M, Lao S (2012) Histogram of oriented normal vectors for object recognition with a depth sensor. In: Asian conference on computer vision. Springer, Berlin, pp 525–538CrossRefGoogle Scholar
  14. 14.
    El-Alfy H, Mitsugami I, Yagi Y (2017) Gait recognition based on normal distance maps. IEEE Trans CybernGoogle Scholar
  15. 15.
    Gauss KF (1902) General investigations of curved surfaces of 1827 and 1825Google Scholar
  16. 16.
    Hazewinkel M (2001) Encyclopaedia of mathematics, vol 13. Springer, BerlinzbMATHGoogle Scholar
  17. 17.
    Chhatrala R, Patil S, Lahudkar S, Jadhav DV (2017) Sparse multilinear Laplacian discriminant analysis for gait recognition. Pattern Anal Appl pp 1–14Google Scholar
  18. 18.
    Xu D, Huang Y, Zeng Z, Xu X (2012) Human gait recognition using patch distribution feature and locality-constrained group sparse representation. IEEE Trans Image Process 21(1):316–326MathSciNetCrossRefGoogle Scholar
  19. 19.
    Sarkar S, Phillips P, Liu Z, Vega IR, Grother P, Bowyer K (2005) The humanid gait challenge problem: data sets, performance, and analysis. IEEE Trans Pattern Anal Mach Intell 27:166–177Google Scholar
  20. 20.
    Iwama H, Okumura M, Makihara Y, Yagi Y (2012) The ou-isir gait database comprising the large population dataset and performance evaluation of gait recognition. IEEE Trans Inf Forensics Secur 7(5):1511–1521CrossRefGoogle Scholar
  21. 21.
    Wang C, Zhang J, Pu J, Yuan X, Wang L (2010) Chrono-gait image: a novel temporal template for gait recognition. In: European conference on computer vision. Springer, Berlin, pp 257–270CrossRefGoogle Scholar
  22. 22.
    Guan Y, Li CT, Roli F (2015) On reducing the effect of covariate factors in gait recognition: a classifier ensemble method. IEEE Trans Pattern Anal Mach Intell 37(7):1521–1528CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Risil Chhatrala
    • 1
  • Shailaja Patil
    • 1
  • Dattatray V. Jadhav
    • 2
  1. 1.Rajarshi Sahu College of EngineeringSavitribai Phule Pune UniversityPuneIndia
  2. 2.Directorate of Technical EducationMumbaiIndia

Personalised recommendations