UGC Approved Journal no 63975(19)

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Published in:

Volume 6 Issue 5
May-2019
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIREY06132


Registration ID:
312783

Page Number

696-701

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Title

Literature on Gait Dynamics Normalization Based Gait Recognition

Authors

Abstract

Potential sources for gait biometrics can be seen to derive from two aspects: gait shape and gait dynamics. Authors show that improved gait recognition can be achieved after normalization of dynamics and focusing on the shape information. Authors normalize for gait dynamics using a generic walking model, as captured by a population Hidden Markov Model (pHMM) defined for a set of individuals. The states of this pHMM represent gait stances over one gait cycle and the observations are the silhouettes of the corresponding gait stances. For each sequence, authors first use Viterbi decoding of the gait dynamics to arrive at one dynamics-normalized, averaged, gait cycle of fixed length. The distance between two sequences is the distance between the two corresponding dynamics-normalized gait cycles, which authors quantify by the sum of the distances between the corresponding gait stances. Distances between two silhouettes from the same generic gait stance are computed in the linear discriminant analysis space so as to maximize the discrimination between persons, while minimizing the variations of the same subject under different conditions. The distance computation is constructed so that it is invariant to dilations and erosions of the silhouettes. This helps us handle variations in silhouette shape that can occur with changing imaging conditions. Authors present results on three different, publicly available, data sets. First, authors consider the HumanID Gait Challenge data set, which is the largest gait benchmarking data set that is available (122 subjects), exercising five different factors, i.e., viewpoint, shoe, surface, carrying condition, and time. Authors significantly improve the performance across the hard experiments involving surface change and briefcase carrying conditions. Second, authors also show improved performance on the UMD gait data set that exercises time variations for 55 subjects. Third, on the CMU Mobo data set, authors show results for matching across different walking speeds. It is worth noting that there was no separate training for the UMD and CMU data sets.

Key Words

Biometrics, Data sets, Gait dynamics normalization, Gait recognition, Hidden Markov Model.

Cite This Article

"Literature on Gait Dynamics Normalization Based Gait Recognition", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.696-701, May-2019, Available :http://www.jetir.org/papers/JETIREY06132.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Literature on Gait Dynamics Normalization Based Gait Recognition", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp696-701, May-2019, Available at : http://www.jetir.org/papers/JETIREY06132.pdf

Publication Details

Published Paper ID: JETIREY06132
Registration ID: 312783
Published In: Volume 6 | Issue 5 | Year May-2019
DOI (Digital Object Identifier):
Page No: 696-701
Country: -, -, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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