Gait recognition using static, activity-specific parameters

Abstract
A gait-recognition technique that recovers static body and stride parameters of subjects as they walk is presented. This approach is an example of an activity-specific biometric: 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 — re- lated to mutual information — as opposed to reporting a percent correct with a limited database. This metric pre- dicts how well a given feature vector will filter identity in a large population. We test the utility of a variety of body and stride parameters recovered in different viewing condi- tions on a database consisting of 15 to 20 subjects walking at both an angled and frontal-parallel view with respect to the camera, both indoors and out. We also analyze motion- capture data of the subjects to discover whether confusion in the parameters is inherently a physical or a visual mea- surement error property.

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