Gait Recognition Using Static, Activity-Specific Parameters

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 -- related to mutual information -- as opposed to reporting a percent correct with a limited database. This metric predicts 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 conditions 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 motioncapture data of the subjects to discover whether confusion in the parameters is inherently a physical or ...

• Type: Conference
• Year: 2001
• Where: CVPR
• Authors: Aaron F. Bobick, Amos Y. Johnson
• Keywords: Computer Vision, CVPR 2001, Expected Confusion, Feature Vector, Percent Correct, Static Body, Visual Measurement Error