Regional registration for expression resistant 3-D face recognition
IEEE Transactions on Information Forensics and Security
Automatic face segmentation and facial landmark detection in range images
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A robust method for nose detection under various conditions
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part I
Robust 3D face recognition based on resolution invariant features
Pattern Recognition Letters
Fast and Accurate 3D Face Recognition
International Journal of Computer Vision
A simulated annealing method based on a specialised evolutionary algorithm
Applied Soft Computing
2D representation of facial surfaces for multi-pose 3D face recognition
Pattern Recognition Letters
Distinguishing Facial Features for Ethnicity-Based 3D Face Recognition
ACM Transactions on Intelligent Systems and Technology (TIST)
Geometrical descriptors for human face morphological analysis and recognition
Robotics and Autonomous Systems
On the simultaneous recognition of identity and expression from BU-3DFE datasets
Pattern Recognition Letters
3D face recognition using an expression insensitive dynamic mask
ICISP'12 Proceedings of the 5th international conference on Image and Signal Processing
Robust sparse bounding sphere for 3D face recognition
Image and Vision Computing
Efficient 3D face recognition handling facial expression and hair occlusion
Image and Vision Computing
Computer Vision and Image Understanding
Multi-pose 3D face recognition based on 2D sparse representation
Journal of Visual Communication and Image Representation
Saliency-guided 3D head pose estimation on 3D expression models
Proceedings of the 15th ACM on International conference on multimodal interaction
An efficient 3D face recognition approach using local geometrical signatures
Pattern Recognition
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This paper presents a novel automatic framework to perform 3D face recognition. The proposed method uses a Simulated Annealing-based approach (SA) for range image registration with the Surface Interpenetration Measure (SIM), as similarity measure, in order to match two face images. The authentication score is obtained by combining the SIM values corresponding to the matching of four different face regions: circular and elliptical areas around the nose, forehead, and the entire face region. Then, a modified SA approach is proposed taking advantage of invariant face regions to better handle facial expressions. Comprehensive experiments were performed on the FRGC v2 database, the largest available database of 3D face images composed of 4,007 images with different facial expressions. The experiments simulated both verification and identification systems and the results compared to those reported by state-of-the-art works. By using all of the images in the database, a verification rate of 96.5 percent was achieved at a False Acceptance Rate (FAR) of 0.1 percent. In the identification scenario, a rank-one accuracy of 98.4 percent was achieved. To the best of our knowledge, this is the highest rank-one score ever achieved for the FRGC v2 database when compared to results published in the literature.