Pose Normalization via Learned 2D Warping for Fully Automatic Face Recognition
We present a novel approach to pose-invariant face recognition that handles continuous pose variations, is not database-specific, and achieves high accuracy without any manual intervention. Our method uses multidimensional Gaussian process regression to learn a nonlinear mapping function from the 2D shapes of faces at any non-frontal pose to the corresponding 2D frontal face shapes. We use this mapping to take an input image of a new face at an arbitrary pose and pose-normalize it, generating a...[Show more]
|Collections||ANU Research Publications|
|Source:||British Machine Vision Conference BMVC 2011 proceedings|
|01_Asthana_Pose_Normalization_via_Learned_2011.pdf||1.6 MB||Adobe PDF||Request a copy|
Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.