Gaussian Representation for 3D Point Based Head Model Classification Based on Generalized Minimax Algorithm

3D point based head model classification gains more and more attention due to its useful application in mechanical engineering, computer vision and biology. In this paper, we propose a new approach to perform 3D point based head model classification. Each head model is represented by a Gaussian distribution with the constrained boundary. The constrained boundary is the minimal enclosing ellipsoid of 3D head model. In order to estimate the parameter of the Gaussian distribution, we propose the generalized minimax algorithm. The generalized minimax algorithm for parameter estimation saves a lot of computational cost when comparing with traditional minimax algorithm. The Mahalanobis distance is applied to measure the similarity between two Gaussian distributions with respect to two head models. The experiments show that the new approach works well during the process of 3D point based head model classification.