Abstract:
Estimating 3-dimensional head pose from a single 2D image is a challenging task with extensive applications. Existing approaches lack the capability to deal with multiple...Show MoreMetadata
Abstract:
Estimating 3-dimensional head pose from a single 2D image is a challenging task with extensive applications. Existing approaches lack the capability to deal with multiple pose-related and — unrelated factors in a uniform way. Most of them can provide only 1-dimensional yaw estimation and suffer from limited representation ability for out-of-sample testing inputs. These drawbacks limit their performance especially on faces in-the-wild. To address this problem, we propose a new head pose estimation approach, which models the pose variation as a 3-sphere manifold embedded in the high-dimensional feature space. It can uniformly factorize multiple factors in an instance parametric subspace, where novel inputs can be synthesized under a generative framework. Moreover, our approach can effectively avoid the manifold degradation issue by learning the embedding in a novel direction. The pose estimation results on multiple databases demonstrate the superior performance of our approach compared with the state-of-the-arts.
Published in: 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)
Date of Conference: 04-08 May 2015
Date Added to IEEE Xplore: 23 July 2015
Electronic ISBN:978-1-4799-6026-2