Applied multivariate statistical analysis
Applied multivariate statistical analysis
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face verification from 3D and grey level clues
Pattern Recognition Letters
Use of depth and colour eigenfaces for face recognition
Pattern Recognition Letters
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Integrating Range and Texture Information for 3D Face Recognition
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Performance of Geometrix ActiveID^TM 3D Face Recognition Engine on the FRGC Data
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Strategies and Benefits of Fusion of 2D and 3D Face Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Intraclass Retrieval of Nonrigid 3D Objects: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
2D and 3D face recognition: A survey
Pattern Recognition Letters
Real-time Stereo Face Recognition by Fusing Appearance and Depth Fisherfaces
Journal of VLSI Signal Processing Systems
Journal of Cognitive Neuroscience
Keypoint Detection and Local Feature Matching for Textured 3D Face Recognition
International Journal of Computer Vision
Automatic 3D face recognition from depth and intensity Gabor features
Pattern Recognition
A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition
Computer Vision and Image Understanding
Face recognition from 2D and 3D images using 3D Gabor filters
Image and Vision Computing
Robust face recognition using 2D and 3D data: Pose and illumination compensation
Pattern Recognition
Expression-invariant 3D face recognition
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Evaluation of automatic 4D face recognition using surface and texture registration
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Learning to fuse 3d+2d based face recognition at both feature and decision levels
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
Face localization and authentication using color and depth images
IEEE Transactions on Image Processing
Face recognition in 2D and 2.5D using ridgelets and photometric stereo
Pattern Recognition
Integration of multi-feature fusion and dictionary learning for face recognition
Image and Vision Computing
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Most of the existing approaches of multimodal 2D+3D face recognition exploit the 2D and 3D information at the feature or score level. They do not fully benefit from the dependency between modalities. Exploiting this dependency at the early stage is more effective than the later stage. Early fusion data contains richer information about the input biometric than the compressed features or matching scores. We propose an image recombination for face recognition that explores the dependency between modalities at the image level. Facial cues from the 2D and 3D images are recombined into a more independent and discriminating data by finding transformation axes that account for the maximal amount of variances in the images. We also introduce a complete framework of multimodal 2D+3D face recognition that utilizes the 2D and 3D facial information at the enrollment, image and score levels. Experimental results based on NTU-CSP and Bosphorus 3D face databases show that our face recognition system using image recombination outperforms other face recognition systems based on the pixel- or score-level fusion.