Real-time Gender Classification From Human Gait for Arbitrary View Angles
Date Issued
2009
Date
2009
Author(s)
Chang, Ping-Chieh
Abstract
In this thesis, we investigate an important but understudied problem, gender classification from human gaits. And we have proved the ability of using GEI (Gait Energy Image) as a representation of human gait for arbitrary view angles. Using GEI as a discriminative feature, we constructed angle classifiers and gender classifiers from different approaches. Experiments have shown that our system achieved a good performance and is able to be applied to real-world application.
Subjects
Gender classification
Human Gait
GEI (Gait Energy Image)
LDA
Fisher-Boosting
Visual Surveillance
Type
thesis
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