A comparison of data representation types, feature types and fusion techniques for 3D face biometry

This paper focuses on the problems of person identification and authentication using registered 3D face data. The face surface geometry is represented alternately as a point cloud, a depth image or as voxel data. Various local or global feature sets are extracted, such as DFT/DCT coefficients, ICA- and NMF- projections, which results in a rich repertoire of representations/features. The identification and authentication performance of the individual schemes are compared. Fusion schemes are invoked, to improve the performance especially in the case when there are only few samples per subject.