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IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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3D Face Recognition Using Two Views Face Modeling and Labeling
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Matching 2.5D Face Scans to 3D Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A 3D Facial Expression Database For Facial Behavior Research
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Analyzing Facial Expressions Using Intensity-Variant 3D Data For Human Computer Interaction
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A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition
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Computer-Aided Design
Automatic 3d facial expression analysis in videos
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Automatic 3D facial region retrieval from multi-pose facial datasets
EG 3DOR'09 Proceedings of the 2nd Eurographics conference on 3D Object Retrieval
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Many of the contemporary 3D facial recognition and facial expression recognition algorithms depend on locating primary facial features, such as the eyes, nose, or lips. Others are dependent on determining the pose of the face. We propose a novel method for limiting the search space needed to find these "interesting features." We then show that our algorithm can be used in conjunction with surface labeling to robustly determine the pose of a face. Our approach does not require any type of training. It is pose-invariant and can be applied to both manually cropped models and raw range data, which can include the neck, ears, shoulders, and other noise. We applied the proposed algorithm to our created 3D range model database, the experiments show the promising results to classify individual faces and individual facial expressions.