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

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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.

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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.

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Authors and Affiliations

  1. Instituto Politécnico Nacional, ESIME Culhuacan, Coyoacan, CDMX, Mexico

    Jose Portillo-Portillo, Gabriel Sanchez-Perez, Hector Perez-Meana, Jesus Olivares-Mercado, Karina Toscano-Medina & Mariko Nakano-Miyatake

  2. Department of Computer Science, University of Warwick, Coventry, UK

    Roberto Leyva & Victor Sanchez

Authors
  1. Jose Portillo-Portillo
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  2. Roberto Leyva
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  3. Victor Sanchez
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  4. Gabriel Sanchez-Perez
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  5. Hector Perez-Meana
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  7. Karina Toscano-Medina
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  8. Mariko Nakano-Miyatake
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Correspondence to Hector Perez-Meana.

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Portillo-Portillo, J., Leyva, R., Sanchez, V. et al. A view-invariant gait recognition algorithm based on a joint-direct linear discriminant analysis. Appl Intell 48, 1200–1217 (2018). https://doi.org/10.1007/s10489-017-1043-8

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