Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Facial Expression Analysis in E-Learning Systems " The Problems and Feasibility
ICALT '05 Proceedings of the Fifth IEEE International Conference on Advanced Learning Technologies
Fast Transformation-Invariant Component Analysis
International Journal of Computer Vision
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Incremental Tensor Subspace Learning and Its Applications to Foreground Segmentation and Tracking
International Journal of Computer Vision
Probabilistic index histogram for robust object tracking
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Correlation-based incremental visual tracking
Pattern Recognition
Multiple kernel learning for emotion recognition in the wild
Proceedings of the 15th ACM on International conference on multimodal interaction
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Image "appearance" may change over time due to a variety of causes such as 1) object or camera motion; 2) generic photometric events including variations in illumination (e.g. shadows) and specular reflections; and 3) "iconic changes" which are specific to the objects being viewed and include complex occlusion events and changes in the material properties ofthe objects. We propose a general framework for representing and recovering these "appearance changes" in an image sequence as a "mixture" of different causes. The approach generalizes previous work on optical flow to provide a richer description of image events and more reliable estimates of image motion.