Model-based object pose in 25 lines of code
International Journal of Computer Vision - Special issue: image understanding research at the University of Maryland
Active shape models—their training and application
Computer Vision and Image Understanding
Automatic Interpretation and Coding of Face Images Using Flexible Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bundle Adjustment - A Modern Synthesis
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
Robust Real-Time Face Detection
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Accurate Head Pose Tracking in Low Resolution Video
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
ZNCC-based template matching using bounded partial correlation
Pattern Recognition Letters
Evaluation of Features Detectors and Descriptors based on 3D Objects
International Journal of Computer Vision
Large head movement tracking using sift-based registration
Proceedings of the 15th international conference on Multimedia
A two-stage head pose estimation framework and evaluation
Pattern Recognition
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
SBA: A software package for generic sparse bundle adjustment
ACM Transactions on Mathematical Software (TOMS)
Head Pose Estimation in Computer Vision: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generic and real-time structure from motion using local bundle adjustment
Image and Vision Computing
IEEE Transactions on Circuits and Systems for Video Technology
Three-dimensional face pose detection and tracking using monocular videos: tool and application
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Real-time face tracking and pose estimation with partitioned sampling and relevance vector machine
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
In the Eye of the Beholder: A Survey of Models for Eyes and Gaze
IEEE Transactions on Pattern Analysis and Machine Intelligence
RSMAT: Robust simultaneous modeling and tracking
Pattern Recognition Letters
Sequential non-rigid structure-from-motion with the 3D-implicit low-rank shape model
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Bundle adjustment in the large
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Conjugate gradient bundle adjustment
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Real-time combined 2D+3D active appearance models
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Face tracking with automatic model construction
Image and Vision Computing
Real-time system for monitoring driver vigilance
IEEE Transactions on Intelligent Transportation Systems
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This paper proposes a new method to perform real-time face pose estimation for +/-90^o yaw rotations and under low light conditions. The algorithm works on the basis of a completely automatic and run-time incremental 3D face modelling. The model is initially made up upon a set of 3D points derived from stereo grey-scale images. As new areas of the subject face appear to the cameras, new 3D points are automatically added to complete the model. In this way, we can estimate the pose for a wide range of rotation angles, where typically 3D frontal points are occluded. We propose a new feature re-registering technique which combines views of both cameras of the stereo rig in a smart way in order to perform a fast and robust tracking for the full range of yaw rotations. The Levenberg-Marquardt algorithm is used to recover the pose and a RANSAC framework rejects incorrectly tracked points. The model is continuously optimised in a bundle adjustment process that reduces the accumulated error on the 3D reconstruction. The intended application of this work is estimating the focus of attention of drivers in a simulator, which imposes challenging requirements. We validate our method on sequences recorded in a naturalistic truck simulator, on driving exercises designed by a team of psychologists.