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

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

Abstract

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.

Keywords

Gait recognition Normal distance map Sparse multilinear discriminant analysis Biometrics etc 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

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

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