IEEE Transactions on Pattern Analysis and Machine Intelligence
3D face recognition by constructing deformation invariant image
Pattern Recognition Letters
Using multi-instance enrollment to improve performance of 3D face recognition
Computer Vision and Image Understanding
3D Face Recognition by Local Shape Difference Boosting
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Measuring the Similarity of Vector Fields Using Global Distributions
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Facial expression biometrics using statistical shape models
EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in biometric systems: a signal processing perspective
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Automatic face segmentation and facial landmark detection in range images
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
2D representation of facial surfaces for multi-pose 3D face recognition
Pattern Recognition Letters
Geometric graph comparison from an alignment viewpoint
Pattern Recognition
Robust sparse bounding sphere for 3D face recognition
Image and Vision Computing
Reshaping 3D facial scans for facial appearance modeling and 3D facial expression analysis
Image and Vision Computing
Multi-pose 3D face recognition based on 2D sparse representation
Journal of Visual Communication and Image Representation
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Human face recognition based on 3D surface matching is promising for overcoming the limitations of current 2D image-based face recognition systems. The 3D shape is invariant to the pose and lighting changes, but not invariant to the non-rigid facial movement, such as expressions. Collecting and storing multiple templates for each subject in a large database (associated with various expressions) is not practical. We present a facial surface modeling and matching scheme to match 2.5D test scans in the presence of both non-rigid deformations and large pose changes (multiview) to a neutral expression 3D face model. A 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 personspecific (3D) deformable model is built for each subject in the gallery w.r.t. the control group by combining the templates with synthesized deformations. By fitting this generative deformable model to a test scan, the proposed approach is able to handle expressions and large pose changes simultaneously. Experimental results demonstrate that the proposed matching scheme based on deformation modeling improves the matching accuracy.