Determining camera parameters from the perspective projection of a rectangle
Pattern Recognition
Camera Calibration by Vanishing Lines for 3-D Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Geometric computation for machine vision
Geometric computation for machine vision
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Iterative pose estimation using coplanar feature points
Computer Vision and Image Understanding
3D Shape Reconstruction by Using Vanishing Points
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Approaches to Feature-Based Object Recognition
International Journal of Computer Vision
Object Pose: The Link between Weak Perspective,Paraperspective, and Full Perspective
International Journal of Computer Vision
SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
Frontal-view face detection and facial features extraction using color and morphological operations
Pattern Recognition Letters
Model-Based Recognition of 3D Objects from Single Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Computer and Robot Vision
SoftPOSIT: Simultaneous Pose and Correspondence Determination
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Vanishing Point Detection without Any A Priori Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
LAFTER: Lips and Face Real-Time Tracker
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Parametrized structure from motion for 3D adaptive feedback tracking of faces
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Computing 3-D head orientation from a monocular image sequence
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Interpreting Face Images Using Active Appearance Models
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Pose Estimation using 3D View-Based Eigenspaces
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Capturing Subtle Facial Motions in 3D Face Tracking
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Editorial: The age of human computer interaction
Image and Vision Computing
Head orientation estimation for covert-tracking robot
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
A new representation method of head images for head pose estimation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
3D head pose estimation and tracking using particle filtering and ICP algorithm
AMDO'10 Proceedings of the 6th international conference on Articulated motion and deformable objects
Model free head pose estimation using stereovision
Pattern Recognition
Real-time head pose estimation using random regression forests
CCBR'11 Proceedings of the 6th Chinese conference on Biometric recognition
Improving accuracy in face tracking user interfaces using consumer devices
Proceedings of the 1st Annual conference on Research in information technology
Proceedings of the 4th International Conference on Automotive User Interfaces and Interactive Vehicular Applications
Hi-index | 0.00 |
Head pose estimation is a key task for visual surveillance, HCI and face recognition applications. In this paper, a new approach is proposed for estimating 3D head pose from a monocular image. The approach assumes the full perspective projection camera model. Our approach employs general prior knowledge of face structure and the corresponding geometrical constraints provided by the location of a certain vanishing point to determine the pose of human faces. To achieve this, eye-lines, formed from the far and near eye corners, and mouth-line of the mouth corners are assumed parallel in 3D space. Then the vanishing point of these parallel lines found by the intersection of the eye-line and mouth-line in the image can be used to infer the 3D orientation and location of the human face. In order to deal with the variance of the facial model parameters, e.g. ratio between the eye-line and the mouth-line, an EM framework is applied to update the parameters. We first compute the 3D pose using some initially learnt parameters (such as ratio and length) and then adapt the parameters statistically for individual persons and their facial expressions by minimizing the residual errors between the projection of the model features points and the actual features on the image. In doing so, we assume every facial feature point can be associated to each of features points in 3D model with some a posteriori probability. The expectation step of the EM algorithm provides an iterative framework for computing the a posterori probabilities using Gaussian mixtures defined over the parameters. The robustness analysis of the algorithm on synthetic data and some real images with known ground-truth are included.