@inproceedings{1433a784f81643f89a133008166800d2,
title = "View-invariant gait recognition using a joint-DLDA framework",
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.",
author = "Jose Portillo and Roberto Leyva and Victor Sanchez and Gabriel Sanchez and Hector Perez-Meana and Jesus Olivares and Karina Toscano and Mariko Nakano",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016 ; Conference date: 02-08-2016 Through 04-08-2016",
year = "2016",
doi = "10.1007/978-3-319-42007-3_34",
language = "Ingl{\'e}s",
isbn = "9783319420066",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "398--408",
editor = "Moonis Ali and Hamido Fujita and Jun Sasaki and Masaki Kurematsu and Ali Selamat",
booktitle = "Trends in Applied Knowledge-Based Systems and Data Science - 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, Proceedings",
address = "Alemania",
}