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
IEEE Transactions on Intelligent Transportation Systems
Cluster-based distributed face tracking in camera networks
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
An efficient 3d head pose inference from videos
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
VACE multimodal meeting corpus
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
Eye gesture recognition on portable devices
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Image and Vision Computing
3D model-based face recognition in video
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
High detail flexible viewpoint facial video from monocular input using static geometric proxies
Proceedings of the 6th International Conference on Computer Vision / Computer Graphics Collaboration Techniques and Applications
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Estimating 3D head poses accurately in low resolution video is a challenging vision task because it is difficult to find continuous one-to-one mapping from personindependent low resolution visual representation to head pose parameters. We propose to track head poses by modeling the shape-free facial textures acquired from the video with subspace learning techniques. In particular, we propose to model the facial appearance variations online by incremental weighted PCA subspace with forgetting mechanism, and we do the tracking in an annealed particle filtering framework. Experiments show that, the tracking accuracy of our approach outperforms past visual face tracking algorithms especially in low resolution videos.