Abstract.
3D image data provide several advantages than 2D data for face recognition and overcome many problems with 2D intensity images based methods. In this paper, we propose a novel approach to 3D-based face recognition. First, a novel representation, called intrinsic features, is presented to encode local 3D shapes. It describes complementary non- relational features to provide an intrinsic representation of faces. This representation is extracted after alignment, and is invariant to transla- tion, rotation and scale. Without reduction, tens of thousands of intrinsic features can be produced for a face, but not all of them are useful and equally important. Therefore, in the second part of the work, we intro- duce a learning method for learning most effective local features and combining them into a strong classifier using an AdaBoost learning pro- cedure. Experimental results are performed on a large 3D face database obtained with complex illumination, pose and expression variations. The results demonstrate that the proposed approach produces consistently better results than existing methods.