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Face pose estimation and its application in video shot selection

In this paper, a face pose estimation method and its application in video shot selection for face image preprocessing is introduced. The pose estimator is learned by a boosting regression algorithm called SquareLev.R that learns poses from simple Haar-type features. It consists of two tree structured subsystems for the left-right angle and up-down angle respectively. As a specific application in video based face recognition, the best shot selection problem is discussed, which results in a real-time system that can automatically select the most frontal face from a video sequence.