An Iterative Regression Approach for Face Pose Estimation from RGB Images

2017/09/10 by Wenye He, He, Wenye
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #cs.CV

paper · pdf · doi:10.48550/arxiv.1709.03170

arxiv created 2017/09/10 · arxiv updated 2017/09/12

Abstract

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.

Related

• img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation
• A Generic Regression Framework for Pose Recognition on Color and Depth Images
• Human Pose Regression by Combining Indirect Part Detection and Contextual Information
• A Survey of Efficient Regression of General-Activity Human Poses from Depth Images
• RankPose: Learning Generalised Feature with Rank Supervision for Head Pose Estimation