Graphical Templates for Model Registration
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Object Detection in Images by Components
IEEE Transactions on Pattern Analysis and Machine Intelligence
3-D Motion Estimation in Model-Based Facial Image Coding
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Viterbi decoding algorithm
IEEE Transactions on Information Theory
Face detection and tracking in a video by propagating detection probabilities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time elliptical head contour detection under arbitrary pose and wide distance range
Journal of Visual Communication and Image Representation
Real-time face tracking system using adaptive face detector and Kalman filter
HCI'07 Proceedings of the 12th international conference on Human-computer interaction: intelligent multimodal interaction environments
High-performance template tracking
Journal of Visual Communication and Image Representation
Effective detector and kalman filter based robust face tracking system
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
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In this paper, we consider the problem of tracking a moving human face in front of a video camera in real-time for a model-based coding application. The 3D head tracking in a MBC system could be implemented sequentially as 2D location tracking, coarse 3D orientation estimation and accurate 3D motion estimation. This work focuses on the 2D location tracking of one face object through continuously using a face detector. The face detection scheme is based on a boosted cascade of simple Haar-like feature classifiers. Although such a detector demonstrated rapid processing speed, high detection rate can only be achieved for rather strictly near front faces. This introduces the 'loss of tracking' problem when used in 2D tracking. This paper suggests an easy method of solving the pose problem by using the technique of Dynamic Programming. The Haar-like facial features used in the 2D face detector are spatially arranged into a 1D deformable face graph and the Dynamic Programming matching is used to handle the 'loss of track' problem. Dynamic Programming matches the deformed version of the face graph extracted from a rotated face with the template taken online before 'loss of tracking' happens. Since the deformable face graph covers a big pose variation, the developed technique is robust in tracking rotated faces. Embedding Haar-like facial features into a deformable face graph is the key feature of our tracking scheme. A real time tracking system based on this technique has been set up and tested. Encouraging results have been got and are reported.