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
IEEE Transactions on Pattern Analysis and Machine Intelligence
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)
A Survey Of Approaches To Three-Dimensional Face Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
3D Model-Assisted Face Recognition in Video
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
Deformation Invariant Image Matching
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Multiple Nose Region Matching for 3D Face Recognition under Varying Facial Expression
IEEE Transactions on Pattern Analysis and Machine Intelligence
Description and retrieval of 3D face models using iso-geodesic stripes
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
On bending invariant signatures for surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
A 3D face matching framework for facial curves
Graphical Models
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Face recognition has been addressed both in 2D, using still images or video sequences, and in 3D using three-dimensional face models. In this paper, we propose an original framework which provides a description capable to support 3D-3D face recognition as well as to directly compare 2D face images against 3D face models. This representation is extracted by measuring geodesic distances in 3D and 2D. In 3D, the geodesic distance between two points on a surface is computed as the length of the shortest path connecting the two points on the model. In 2D, the geodesic distance between two pixels is computed based on the differences of gray level intensities along the segment connecting the two pixels in the image. Experimental results are reported for 3D-3D and 2D-3D face recognition, in order to demonstrate the viability of the proposed approach.