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It is common to view people in real applications walking in arbitrarydirections, holding items, or wearing heavy coats. These factors are challengesin gait-based application methods because they significantly change a person'sappearance. This paper proposes a novel method for classifying human gender inreal time using gait information. The use of an average gait image (AGI),rather than a gait energy image (GEI), allows this method to be computationallyefficient and robust against view changes. A viewpoint (VP) model is createdfor automatically determining the viewing angle during the testing phase. Adistance signal (DS) model is constructed to remove any areas with anattachment (carried items, worn coats) from a silhouette to reduce theinterference in the resulting classification. Finally, the human gender isclassified using multiple view-dependent classifiers trained using a supportvector machine. Experiment results confirm that the proposed method achieves ahigh accuracy of 98.8% on the CASIA Dataset B and outperforms the recentstate-of-the-art methods.

Real-time and robust multiple-view gender classification using gait features in video surveillance