  It is common to view people in real applications walking in arbitrary
directions, holding items, or wearing heavy coats. These factors are challenges
in gait-based application methods because they significantly change a person's
appearance. This paper proposes a novel method for classifying human gender in
real time using gait information. The use of an average gait image (AGI),
rather than a gait energy image (GEI), allows this method to be computationally
efficient and robust against view changes. A viewpoint (VP) model is created
for automatically determining the viewing angle during the testing phase. A
distance signal (DS) model is constructed to remove any areas with an
attachment (carried items, worn coats) from a silhouette to reduce the
interference in the resulting classification. Finally, the human gender is
classified using multiple view-dependent classifiers trained using a support
vector machine. Experiment results confirm that the proposed method achieves a
high accuracy of 98.8% on the CASIA Dataset B and outperforms the recent
state-of-the-art methods.
