Paper Info
Title
A view-invariant gait recognition algorithm based on a joint-direct linear discriminant analysis.
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.
Year
DOI
Venue
2018
https://doi.org/10.1007/s10489-017-1043-8
Appl. Intell.
Keywords
Field
DocType
Gait recognition scheme,View-invariant method,Gait energy image,Direct linear discriminant analysis (DLDA),Euclidean distance
Dimensionality reduction,Gait,Computer science,Artificial intelligence,Recognition algorithm,Computer vision,Feature vector,Pattern recognition,Euclidean distance,Invariant (mathematics),Linear discriminant analysis,Machine learning,Computational complexity theory
Journal
Volume
Issue
ISSN
48
5
0924-669X
Citations 
PageRank 
References 
3
0.38
33
Authors
8
Name
Order
Citations
PageRank
José Portillo-Portillo183.70
Roberto Leyva2203.80
Victor Sanchez314431.22
Gabriel Sanchez-Perez42813.00
Hector Perez-Meana513423.60
Jesus Olivares-Mercado6157.23
Karina Toscano-Medina7198.94
Mariko Nakano-Miyatake814429.99