Titlebook: Machine Learning Techniques for Gait Biometric Recognition; Using the Ground Rea James Eric Mason,Issa Traoré,Isaac Woungang Book 2016 Spri

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Normalization,y, feature extraction techniques may identify the discriminant features not affected by such variability. However, when it is possible to identify these sources of variability, it may also be possible to use . to expose the important features that would otherwise be hidden due to differences in the conditions at the time of sample collection.

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Applications of Gait Biometrics,lications in industry. Nevertheless, gait biometric recognition continues to be a growing area of interest due to its unobtrusive nature and an increasing number of technologies available for capturing information about the human gait. In this chapter, we explore a variety of . for which gait biometrics may be deployed.

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https://doi.org/10.1007/978-3-319-29088-1Behavioral biometrics; Biometrics Recognition framework; Footstep GRF-based person recognition; GRF Rec

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James Eric Mason,Issa Traoré,Isaac WoungangIntroduces novel machine-learning-based temporal normalization techniques.Bridges research gaps concerning the effect of footwear and stepping speed on footstep GRF-based person recognition.Provides d

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ng speed on footstep GRF-based person recognition.Provides dThis book focuses on how machine learning techniques can be used to analyze and make use of one particular category of behavioral biometrics known as the gait biometric. A comprehensive Ground Reaction Force (GRF)-based Gait Biometrics Reco

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Experimental Analysis,nd, finally, we explore ways in which we may be able to . upon the results discovered. It is hoped that this work will contribute to the present day understanding of GRF recognition and address some of the technical issues facing the deployment of such a system in a real-world setting.

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