3D and 2D face recognition based on image segmentation

In this paper we propose a framework for 3D Face
Recognition System (3DFRS) using segmentation by
grouping of regions of facial images before and after fusion
of two modalities (color and depth images). Firstly, the
detection of face region is based on the localization of nose
tip and integral projection curves. Then, the features
resulting from Principle Component Analyses (PCA)
followed by Enhanced Fisher Model (EFM) are extracted.
Finally, the classification process is performed with two
methods, distance measurement L3 and Support Vector
Machine (SVM). Experiments are performed on the
CASIA3D face database which contains 123 persons under
varying illumination and expression. We have tried to
examine all the variants associated with our algorithms in
order to optimize the maximum our recognition system. The
promising results of the experimental evaluation show that
our proposed approach achieves a high recognition
performance.