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A method for human identification us...

Johnson, Amos Yancy, Jr.

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A method for human identification using static, activity-specific parameters.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	A method for human identification using static, activity-specific parameters./
Author:	Johnson, Amos Yancy, Jr.
Description:	109 p.
Notes:	Director: Aaron F. Bobick.
Contained By:	Dissertation Abstracts International63-03B
Subject:	Computer Science -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3046907
ISBN:	0493612076

A method for human identification using static, activity-specific parameters.
Johnson, Amos Yancy, Jr.

A method for human identification using static, activity-specific parameters. - 109 p.

Director: Aaron F. Bobick.

Thesis (Ph.D.)--Georgia Institute of Technology, 2002.

A method for human identification using static, activity-specific parameters is presented. This method recovers static body and stride parameters during the gait cycles of humans. Our technique is classified as a gait biometric; however, it does not directly analyze dynamic gait patterns, but uses the action of walking to extract relative body and stride parameters. This approach is an example of an <italic>activity-specific biometric</italic>: a method of extracting identifying properties of an individual or of an individual's behavior that is applicable only when a person is performing that specific action. To evaluate our parameters, we derive an expected confusion metric in lieu of reporting recognition rates, which are misleading in limited databases. Given a small, yet representative, set of subjects, the expected-confusion metric allows us to predict the identification uncertainty of a feature vector for a larger population of subject. In addition, after multiplying by a dimensional varying scale factor, this transformed expected-confusion gives us the probability of incorrect identification for the feature vector. Last, we test the utility of a variety of body and stride parameters recovered from different viewing conditions and walking speeds, and we use motion-capture data of subjects to discover whether confusion in the parameters is inherently a physical or a visual measurement error property

ISBN: 0493612076Subjects--Topical Terms:

890869
Computer Science

A method for human identification using static, activity-specific parameters.
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100 1 $a Johnson, Amos Yancy, Jr. $3 1260380
245 1 0 $a A method for human identification using static, activity-specific parameters.
300 $a 109 p.
500 $a Director: Aaron F. Bobick.
500 $a Source: Dissertation Abstracts International, Volume: 63-03, Section: B, page: 1427.
502 $a Thesis (Ph.D.)--Georgia Institute of Technology, 2002.
520 $a A method for human identification using static, activity-specific parameters is presented. This method recovers static body and stride parameters during the gait cycles of humans. Our technique is classified as a gait biometric; however, it does not directly analyze dynamic gait patterns, but uses the action of walking to extract relative body and stride parameters. This approach is an example of an <italic>activity-specific biometric</italic>: a method of extracting identifying properties of an individual or of an individual's behavior that is applicable only when a person is performing that specific action. To evaluate our parameters, we derive an expected confusion metric in lieu of reporting recognition rates, which are misleading in limited databases. Given a small, yet representative, set of subjects, the expected-confusion metric allows us to predict the identification uncertainty of a feature vector for a larger population of subject. In addition, after multiplying by a dimensional varying scale factor, this transformed expected-confusion gives us the probability of incorrect identification for the feature vector. Last, we test the utility of a variety of body and stride parameters recovered from different viewing conditions and walking speeds, and we use motion-capture data of subjects to discover whether confusion in the parameters is inherently a physical or a visual measurement error property
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650 $a Computer Science $3 890869
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856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3046907