Facial pose estimation using a symmetrical feature model
This paper presents a robust approach to performing facial pose estimation by examining the behavior of key facial features over a wide range of poses. Such methods are useful in intelligent vision systems for entertainment, human computer interaction, and security. In our approach, faces of varying pose are automatically detected, eyes and mouth are located and an active shape model is superimposed. A facial pose estimator is developed using predictor models based on the position, size, and symmetry of facial features. By modeling these predictors over pose positions with varying yaw and pitch, excellent results are obtained without the need for complex computationally intensive methods.