  In this paper, we propose a robust 3D face recognition system which can
handle pose as well as occlusions in real world. The system at first takes as
input, a 3D range image, simultaneously registers it using ICP(Iterative
Closest Point) algorithm. ICP used in this work, registers facial surfaces to a
common model by minimizing distances between a probe model and a gallery model.
However the performance of ICP relies heavily on the initial conditions. Hence,
it is necessary to provide an initial registration, which will be improved
iteratively and finally converge to the best alignment possible. Once the faces
are registered, the occlusions are automatically extracted by thresholding the
depth map values of the 3D image. After the occluded regions are detected,
restoration is done by Principal Component Analysis (PCA). The restored images,
after the removal of occlusions, are then fed to the recognition system for
classification purpose. Features are extracted from the reconstructed
non-occluded face images in the form of face normals. The experimental results
which were obtained on the occluded facial images from the Bosphorus 3D face
database, illustrate that our occlusion compensation scheme has attained a
recognition accuracy of 91.30%.
