Unsupervised classifier based on geodesic invariant 3D curve for face surfaces analysis

Here, we intend to introduce new face invariant descriptors, composed by two kinds of features, in order to explore the problem of faces classification. The first kind is defined from the p-order moments of a curvature function of the geodesic curve according to its arc length. The second one describes relative positions between important localities of faces. Two classes Fisher discriminate analysis is applied for a dimension reduction. A two dimensional multi classes Expectation Maximization algorithm (2D-EM) is used to identify the components of the mixture distribution. Then, the classification is obtained after applying the Bayes decision rule which is the most optimal for the minimization of the classification error. Such classification gives the sub groups having homogenous similar faces.