A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Labeling of human face components from range data
CVGIP: Image Understanding
COSMOS-A Representation Scheme for 3D Free-Form Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
3D Human Face Recognition Using Point Signature
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Three-Dimensional Model Based Face Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
An Evaluation of Multimodal 2D+3D Face Biometrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition from 2D and 3D images using 3D Gabor filters
Image and Vision Computing
Expression-invariant 3D face recognition
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
A boosting approach to content-based 3D model retrieval
Proceedings of the 5th international conference on Computer graphics and interactive techniques in Australia and Southeast Asia
3D face recognition by constructing deformation invariant image
Pattern Recognition Letters
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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 translation, 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 introduce a learning method for learning most effective local features and combining them into a strong classifier using an AdaBoost learning procedure. 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.