Solid shape
Modeling of curves and surfaces in CAD/CAM
Modeling of curves and surfaces in CAD/CAM
Two- and three-dimensional patterns of the face
Two- and three-dimensional patterns of the face
Efficient Matching and Indexing of Graph Models in Content-Based Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Mean Shift: A Robust Approach Toward Feature Space Analysis
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)
Face Recognition from 3D Data using Iterative Closest Point Algorithm and Gaussian Mixture Models
3DPVT '04 Proceedings of the 3D Data Processing, Visualization, and Transmission, 2nd International Symposium
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
Feature Sensitive Mesh Segmentation with Mean Shift
SMI '05 Proceedings of the International Conference on Shape Modeling and Applications 2005
Weighted walkthroughs between extended entities for retrieval by spatial arrangement
IEEE Transactions on Multimedia
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In this paper, we propose an original framework for three dimensional face representation and similarity matching. Basic traits of a face are encoded by extracting convex and concave regions from the surface of a face model. A compact graph representation is then constructed from these regions through an original modeling technique capable to quantitatively measure spatial relationships between regions in a three dimensional space and to encode this information in an attributed relational graph. In this way, the structural similarity between two face models is evaluated by matching their corresponding graphs. Experimental results on a 3D face database show that the proposed solution attains high retrieval accuracy and is reasonably robust to facial expression and pose changes.