Abstract
Automatic gender detection is a process of determining the gender of a human according to the characteristic
properties that represent the masculine and feminine attributes of a subject. Automatic gender detection is
used in many areas such as customer behaviour analysis, robust security system construction, resource management,
human-computer interaction, video games, mobile applications, neuro-marketing etc., in which
manual gender detection may be not feasible. In this study, we have developed a fully automatic system that
uses the 3D anthropometric measurements of human subjects for gender detection. A Kinect 3D camera was
used to recognize the human posture, and body metrics are used as features for classification. To classify
the gender, KNN, SVM classifiers and Neural Network were used with the parameters. A unique dataset
gathered from 29 female and 31 male (a total of 60 people) participants was used in the experiment and
the Leave One Out method was used as the cross-validation approach. The maximum accuracy achieved is
96.77% for SVM with an MLP kernel function.
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