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2016 | OriginalPaper | Buchkapitel

View-Invariant Gait Recognition Using a Joint-DLDA Framework

verfasst von : Jose Portillo, Roberto Leyva, Victor Sanchez, Gabriel Sanchez, Hector Perez-Meana, Jesus Olivares, Karina Toscano, Mariko Nakano

Erschienen in: Trends in Applied Knowledge-Based Systems and Data Science

Verlag: Springer International Publishing

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Abstract

In this paper, we propose a new view-invariant framework for gait analysis. The framework profits from the dimensionality reduction advantages of Direct Linear Discriminant Analysis (DLDA) to build a unique view-invariant model. Among these advantages is the capability to tackle the under-sampling problem (USP), which commonly occurs when the number of dimensions of the feature space is much larger than the number of training samples. Our framework employs Gait Energy Images (GEIs) as features to create a single joint model suitable for classification of various angles with high accuracy. Performance evaluations shows the advantages of our framework, in terms of computational time and recognition accuracy, as compared to state-of-the-art view-invariant methods.

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Titel: View-Invariant Gait Recognition Using a Joint-DLDA Framework
verfasst von: Jose Portillo
Roberto Leyva
Victor Sanchez
Gabriel Sanchez
Hector Perez-Meana
Jesus Olivares
Karina Toscano
Mariko Nakano
Verlag: Springer International Publishing
Buch: Trends in Applied Knowledge-Based Systems and Data Science
Print ISBN: 978-3-319-42006-6

Electronic ISBN: 978-3-319-42007-3

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-42007-3_34

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