Human Identification Using Gait Skeletal Joint Distance Features

Int. J. Softw. Sci. Comput. Intell. Pub Date : 2017-10-01 DOI:10.4018/IJSSCI.2017100102

Md Wasiur Rahman, M. Gavrilova

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引用次数: 2

Abstract

Gaitnotonlydefinesthewayapersonwalks,butalsoprovidesinsightsonanindividual’sdaily routine,mentalstateorevencognitivefunction.Theimportanceofincorporatingcognitivebehavior andanalysisinbiometricsystemshasbeennotedrecently.Inthisarticle,authorsdevelopabiometric securitysystemusinggait-basedskeletalinformationobtainedfromMicrosoftKinectv1sensor.The gaitcycleiscalculatedbydetectingthethreeconsecutivelocalminimabetweenthejointdistance ofleftandrightankles.Authorshaveutilizedthedistancefeaturevectorforeachofthejointswith respecttootherjointsinthegaitcycle.Aftermeanandvariancefeaturesareextractedfromthedistance featurevector,theKNNalgorithmisusedforclassificationpurpose.Theclassificationaccuracyofthe authors’approachis93.33%.Experimentalresultsshowthattheproposedapproachachievesbetter recognitionaccuracythenotherstate-of-the-artapproaches.Incorporatinggaitbiometricinasituation awarenesssystemforidentificationofamentalstateisoneofthefuturedirectionsofthisresearch. KeywoRDS Biometric System, Cognitive Function, Feature Distance Vector, Gait, Gait Cycle, K Nearest Neighbors (KNN), Kinect Sensor, Pattern Recognition

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基于步态骨骼关节距离特征的人体识别

Gaitnotonlydefinesthewayapersonwalks，butalsoprovidesinsightsonanindividual 'sdaily routine，mentalstateorevencognitivefunction。Theimportanceofincorporatingcognitivebehavior andanalysisinbiometricsystemshasbeennotedrecently。Inthisarticle，authorsdevelopabiometric securitysystemusinggait-basedskeletalinformationobtainedfromMicrosoftKinectv1sensor。The gaitcycleiscalculatedbydetectingthethreeconsecutivelocalminimabetweenthejointdistance ofleftandrightankles。Authorshaveutilizedthedistancefeaturevectorforeachofthejointswith respecttootherjointsinthegaitcycle。Aftermeanandvariancefeaturesareextractedfromthedistance featurevector，theKNNalgorithmisusedforclassificationpurpose。Theclassificationaccuracyofthe作者approachis93.33%。Experimentalresultsshowthattheproposedapproachachievesbetter recognitionaccuracythenotherstate-of-the-artapproaches。Incorporatinggaitbiometricinasituation awarenesssystemforidentificationofamentalstateisoneofthefuturedirectionsofthisresearch。关键词:生物识别系统，认知功能，特征距离向量，步态，步态周期，K近邻，Kinect传感器，模式识别

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Int. J. Softw. Sci. Comput. Intell.

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