Posture invariant gender classification for 3D human models

We study the behaviorally important task of gender classification based on the human body shape. We propose a new technique to classify by gender human bodies represented by possibly incomplete triangular meshes obtained using laser range scanners. The classification algorithm is invariant of the posture of the human body. Geodesic distances on the mesh are used for classification. Our results indicate that the geodesic distances between the chest and the wrists and the geodesic distances between the lower back and the face are the most important ones for gender classification. The classification is shown to perform well for different postures of the human subjects. We model the geodesic distance distributions as Gaussian distributions and compute the quality of the classification for three standard methods in pattern recognition: linear discriminant functions, Bayesian discriminant functions, and support vector machines. All of the experiments yield high classification accuracy. For instance, when support vector machines are used, the classification accuracy is at least 93% for all of our experiments. This shows that geodesic distances are suitable to discriminate humans by gender.