Information set based gait authentication system

Medikonda, Jeevan; Hanmandlu, M.; Panigrahi, Bijaya Ketan

doi:10.1016/j.neucom.2016.02.009

Cited by 14 publications

(9 citation statements)

References 42 publications

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“…Verlekar et al [146] presented the so called sparse error gait image representation. Medikonda et al [147] introduced generalised new entropy representation. Hu et al [148] proposed an incremental representation based on optical flow named LBP Flow.…”

Section: Clothing Robust Feature Representationsmentioning

confidence: 99%

Robust gait recognition: a comprehensive survey

2018

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Gait recognition has emerged as an attractive biometric technology for the identification of people by analysing the way they walk. However, one of the main challenges of the technology is to address the effects of inherent various intra-class variations caused by covariate factors such as clothing, carrying conditions, and view angle that adversely affect the recognition performance. The main aim of this survey is to provide a comprehensive overview of existing robust gait recognition methods. This is intended to provide researchers with state of the art approaches in order to help advance the research topic through an understanding of basic taxonomies, comparisons, and summaries of the state-of-the-art performances on several widely used gait recognition datasets.

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Section: Clothing Robust Feature Representationsmentioning

confidence: 99%

Robust gait recognition: a comprehensive survey

2018

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“…The degree of belongingness termed as information gain g X x i value associated with an information source value I X x i is calculated using the generalised gain function [34], given by…”

Section: Information Set Theorymentioning

confidence: 99%

“…the information source value and the associated gain function g X x i , i.e. MF value is termed as the information value [34], denoted by…”

Section: Information Set Theorymentioning

confidence: 99%

“…The effective information E X present in I X is calculated using the generalised entropy function [34] as the cumulative information…”

Section: Information Set Theorymentioning

confidence: 99%

“…However, speed estimation itself may be prone to error while fluctuation of speed exists in a gait sequence. To address these aspects, we are motivated to recall the information set based features [33] and generalised new entropy function [34] that facilitate the extraction of two‐fold information set (2FInS) features by combining the spatial and temporal uncertainties at each element of a frame in a gait sequence. These features improve the results of small speed variations but for large speed variations we cash in on the spatial and temporal information.…”

Section: Introductionmentioning

confidence: 99%

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Information set based features for the speed invariant gait recognition

Medikonda

Hanmandlu²,

Ketan³

2017

IET biom.

Self Cite

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A novel speed-invariant gait features called twofold information set (2FInS) features that capture both spatial and temporal variations in a gait cycle are proposed in this study. These features are obtained by applying first histogram of oriented gradients descriptors on the gait images followed by the representation of the underlying possibilistic uncertainty using the Hanman-Jeevan entropy function. The 2FInS features are validated on three databases: CASIA-C, OU-ISIR Treadmill-A and OU-ISIR Treadmill-D using Procrustes distance based classifier. In view of accounting both spatial and temporal information distributed throughout a gait cycle, the results obtained are superior to those of the existing methods.

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Wearable Gait Authentication

Swathi,

Choudhary

et al. 2024

Optimized Predictive Models in Healthcare Using Machine Learning

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Information set based gait authentication system

Cited by 14 publications

References 42 publications

Robust gait recognition: a comprehensive survey

Robust gait recognition: a comprehensive survey

Information set based features for the speed invariant gait recognition

Wearable Gait Authentication

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