3D Face Recognition by Functional Data Analysis

This work proposes the use of functional data analysis to represent 3D faces for recognition tasks. This approach allows exploiting and studying characteristics of the continuous nature of this type of data. The basic idea of our proposal is to approximate the 3D face surface through an expansion of a basis functions set. These functions are used for a global representation of the entire face, and a local representation, where pre-selected face regions are used to construct multiple local epresentations. In both cases, the functions are fitted to the 3D data by means of the least squares method. Univariate attribute selection is finally applied to reduce the dimensionality of the new representation. The recognition experiments prove the validity of the proposed approach, showing competitive results with respect to the state of the art solutions. Moreover, the dimensionality of the data is considerably reduced with respect to the original size, which is one of the goals of using this approach.