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
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
Multiple Nose Region Matching for 3D Face Recognition under Varying Facial Expression
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D face detection using curvature analysis
Pattern Recognition
An Expression Deformation Approach to Non-rigid 3D Face Recognition
International Journal of Computer Vision
Gappy PCA Classification for Occlusion Tolerant 3D Face Detection
Journal of Mathematical Imaging and Vision
Three-Dimensional Occlusion Detection and Restoration of Partially Occluded Faces
Journal of Mathematical Imaging and Vision
Exploring facial expression effects in 3d face recognition using partial ICP
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
A Region Ensemble for 3-D Face Recognition
IEEE Transactions on Information Forensics and Security
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Occlusions over facial surfaces cause performance degradation for face registration and recognition systems. In this work, we propose an occlusion-resistant three-dimensional face registration method. First, the nose area is detected on a probe face using curvedness-weighted convex shape index map. Then, probable eye and mouth patches are detected and checked for validity. An adaptive model is constructed by selecting valid patches of the average face model. Finally, registration is handled with the Iterative Closest Point algorithm, where the adaptive model is used as the reference. The UMB-DB face database is used to evaluate the registration system: The nose detector has 100% and 93.90% accuracy, for the non-occluded and occluded images, respectively. A simple global depth-based recognition experiment is done to evaluate the registration performance: Our adaptive model-based registration scheme improves rank-1 recognition rate by 16%, when compared with the nose-based alignment approach.