3D face recognition by constructing deformation invariant image
Pattern Recognition Letters
Topology-Invariant Similarity of Nonrigid Shapes
International Journal of Computer Vision
Automatic 3D face recognition from depth and intensity Gabor features
Pattern Recognition
A bi-modal face recognition framework integrating facial expression with facial appearance
Pattern Recognition Letters
A novel approach to classification of facial expressions from 3D-mesh datasets using modified PCA
Pattern Recognition Letters
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Optimal estimation and detection in homogeneous spaces
IEEE Transactions on Signal Processing
Ethnicity- and Gender-based Subject Retrieval Using 3-D Face-Recognition Techniques
International Journal of Computer Vision
From 3D Point Clouds to Pose-Normalised Depth Maps
International Journal of Computer Vision
International Journal of Computer Vision
Modeling 3D facial expressions using geometry videos
Proceedings of the international conference on Multimedia
Elastic radial curves to model 3D facial deformations
Proceedings of the ACM workshop on 3D object retrieval
Geodesic Methods in Computer Vision and Graphics
Foundations and Trends® in Computer Graphics and Vision
Computer Vision and Image Understanding
2.5D face recognition using Patch Geodesic Moments
Pattern Recognition
Isometric deformation invariant 3D shape recognition
Pattern Recognition
Efficient 3D face recognition handling facial expression and hair occlusion
Image and Vision Computing
Selecting 3D curves on the nasal surface using AdaBoost for person authentication
EG 3DOR'11 Proceedings of the 4th Eurographics conference on 3D Object Retrieval
Robust learning from normals for 3d face recognition
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
A structured template based 3D face recognition approach
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
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Addressed here is the problem of constructing and analyzing expression-invariant representations of human faces. We demonstrate and justify experimentally a simple geometric model that allows to describe facial expressions as isometric deformations of the facial surface. The main step in the construction of expression-invariant representation of a face involves embedding of the facial intrinsic geometric structure into some low-dimensional space. We study the influence of the embedding space geometry and dimensionality choice on the representation accuracy and argue that compared to its Euclidean counterpart, spherical embedding leads to notably smaller metric distortions. We experimentally support our claim showing that a smaller embedding error leads to better recognition