Recognizing human gender in computer vision: a survey
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
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A realistic appearance-based representation of side-view gait sequences is here introduced. It is based on a prior method where a set of appearance-based features of a gait sample is used for gender recognition. These features are computed from parameter values of ellipses that fit body parts enclosed by regions previously defined while ignoring well-known facts of the human body structure. This work presents an improved regionalization method supported by some adaptive heuristic rules to better adjust regions to body parts. As a result, more realistic ellipses and a more meaningful feature space are obtained. Gender recognition experiments conducted on the CASIA Gait Database show better classification results when using the new features.