Recognition by Linear Combinations of Models
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
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
Surface shape and curvature scales
Image and Vision Computing
Curve and surface fitting with splines
Curve and surface fitting with splines
Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of free-form object representation and recognition techniques
Computer Vision and Image Understanding
Computational Framework for Segmentation and Grouping
Computational Framework for Segmentation and Grouping
Curvature-Augmented Tensor Voting for Shape Inference from Noisy 3D Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Complex EGI: A New Representation for 3-D Pose Determination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Fusion in Biometrics
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Spline Representations in 3-D Vision
Proceedings of the International NSF-ARPA Workshop on Object Representation in Computer Vision
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Overview of the Face Recognition Grand Challenge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Matching 2.5D Face Scans to 3D Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition
Computer Vision and Image Understanding
Categorization of natural scenes: local vs. global information
APGV '06 Proceedings of the 3rd symposium on Applied perception in graphics and visualization
Three-Dimensional Model-Based Object Recognition and Segmentation in Cluttered Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic 3D Face Detection, Normalization and Recognition
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
Efficient and reliable template set matching for 3D object recognition
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Some Mathematical and Representational Aspects of Solid Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rank-Based decision fusion for 3d shape-based face recognition
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
A 3D face matching framework for facial curves
Graphical Models
Automatic 3D face recognition from depth and intensity Gabor features
Pattern Recognition
3D Signatures for Fast 3D Face Recognition
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Extracting Structured Topological Features from 3D Facial Surface: Approach and Applications
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
3D face recognition with sparse spherical representations
Pattern Recognition
Regional registration for expression resistant 3-D face recognition
IEEE Transactions on Information Forensics and Security
An efficient 3D face recognition approach using local geometrical signatures
Pattern Recognition
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We present a unified feature representation of 2.5D pointclouds and apply it to face recognition. The representation integrates local and global geometrical cues in a single compact representation which makes matching a probe to a large database computationally efficient. The global cues provide geometrical coherence for the local cues resulting in better descriptiveness of the unified representation. Multiple rank-0 tensors (scalar features) are computed at each point from its local neighborhood and from the global structure of the 2.5D pointcloud, forming multiple rank-0 tensor fields. The pointcloud is then represented by the multiple rank-0 tensor fields which are invariant to rigid transformations. Each local tensor field is integrated with every global field in a 2D histogram which is indexed by a local field in one dimension and a global field in the other dimension. Finally, PCA coefficients of the 2D histograms are concatenated into a single feature vector. The representation was tested on FRGC V2.0 data set and achieved 93.78% identification and 95.37% verification rate at 0.1% FAR.