K-d trees for semidynamic point sets
SCG '90 Proceedings of the sixth annual symposium on Computational geometry
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
Three-Dimensional Model Based Face Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
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
3D Face Recognition Using 3D Alignment for PCA
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Multiple Nose Region Matching for 3D Face Recognition under Varying Facial Expression
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integration of local and global geometrical cues for 3D face recognition
Pattern Recognition
A Region Ensemble for 3-D Face Recognition
IEEE Transactions on Information Forensics and Security
Fast and Accurate 3D Face Recognition
International Journal of Computer Vision
2D representation of facial surfaces for multi-pose 3D face recognition
Pattern Recognition Letters
Multi-pose 3D face recognition based on 2D sparse representation
Journal of Visual Communication and Image Representation
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We propose a vector representation (called a 3D signature) for 3D face shape in biometrics applications. Elements of the vector correspond to fixed surface points in a face-centered coordinate system. Since the elements are registered to the face, comparisons of vectors to produce match scores can be performed without a probe to gallery alignment step such as an invocation of the iterated closest point (ICP) algorithm in the calculation of each match score. The proposed 3D face recognition method employing the 3D signature ran more than three orders of magnitude faster than a traditional ICP based distance implementation, without sacrificing accuracy. As a result, it is feasible to apply distance based 3D face biometrics to recognition scenarios that, because of computational constraints, may have previously been limited to verification. Our use of more complex shape regions, which is a trivial task with the use of 3D signatures, improves biometric performance over simple spherical cut regions used previously [1]. Experimental results with a large database of 3D images demonstrate the technique and its advantages.