An Expression Deformation Approach to Non-rigid 3D Face Recognition
International Journal of Computer Vision
A 3D face matching framework for facial curves
Graphical Models
Learning a generic 3D face model from 2D image databases using incremental Structure-from-Motion
Image and Vision Computing
Technical Section: Expression modeling for expression-invariant face recognition
Computers and Graphics
Tracking vertex flow and model adaptation for three-dimensional spatiotemporal face analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on recent advances in biometrics
Regional registration for expression resistant 3-D face recognition
IEEE Transactions on Information Forensics and Security
3D deformable face tracking with a commodity depth camera
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
On nonmetric similarity search problems in complex domains
ACM Computing Surveys (CSUR)
A review of recent advances in 3D ear- and expression-invariant face biometrics
ACM Computing Surveys (CSUR)
On the simultaneous recognition of identity and expression from BU-3DFE datasets
Pattern Recognition Letters
Efficient 3D face recognition handling facial expression and hair occlusion
Image and Vision Computing
Multibiometric human recognition using 3D ear and face features
Pattern Recognition
Person identification using full-body motion and anthropometric biometrics from kinect videos
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
An efficient 3D face recognition approach using local geometrical signatures
Pattern Recognition
3D dental biometrics: Alignment and matching of dental casts for human identification
Computers in Industry
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Face recognition based on 3D surface matching is promising for overcoming some of the limitations of current 2D image-based face recognition systems. The 3D shape is generally invariant to the pose and lighting changes, but not invariant to the non-rigid facial movement, such as expressions. Collecting and storing multiple templates to account for various expressions for each subject in a large database is not practical. We propose a facial surface modeling and matching scheme to match 2.5D facial scans in the presence of both non-rigid deformations and pose changes (multiview) to a 3D face template. A hierarchical geodesic-based resampling approach is applied to extract landmarks for modeling facial surface deformations. We are able to synthesize the deformation learned from a small group of subjects (control group) onto a 3D neutral model (not in the control group), resulting in a deformed template. A user-specific (3D) deformable model is built by combining the templates with synthesized deformations. The matching distance is computed by fitting this generative deformable model to a test scan. A fully automatic and prototypic 3D face matching system has been developed. Experimental results demonstrate that the proposed deformation modeling scheme increases the 3D face matching accuracy.