An Iterative Regression Approach for Face Pose Estimation from RGB Images
This work addresses face pose estimation for computer vision applications, presenting an incremental improvement over existing methods.
The paper tackles face pose estimation from RGB images using an iterative regression approach called explicit shape regression, achieving improved accuracy and robustness in facial shape alignment across different scenarios.
This paper presents a iterative optimization method, explicit shape regression, for face pose detection and localization. The regression function is learnt to find out the entire facial shape and minimize the alignment errors. A cascaded learning framework is employed to enhance shape constraint during detection. A combination of a two-level boosted regression, shape indexed features and a correlation-based feature selection method is used to improve the performance. In this paper, we have explain the advantage of ESR for deformable object like face pose estimation and reveal its generic applications of the method. In the experiment, we compare the results with different work and demonstrate the accuracy and robustness in different scenarios.