An Iterative Regression Approach for Face Pose Estimation from RGB Images

TL;DR

This paper introduces an iterative regression method for accurate face pose detection from RGB images, utilizing cascaded learning and shape constraints to improve robustness and precision.

Contribution

It proposes an explicit shape regression framework with a cascaded learning approach, enhancing face pose estimation accuracy over previous methods.

Findings

Demonstrates improved accuracy in face pose detection

Shows robustness across various scenarios

Validates effectiveness through comparative experiments

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.