The Fast and the Flexible: Extended Pseudo Two-Dimensional Warping for Face Recognition

In this work, we propose a novel extension of pseudo 2D image warping (P2DW) which allows for joint alignment and recognition of non-rectified face images. P2DW allows for optimal displacement inference in a simplified setting, but cannot cope with stronger deformations since it is restricted to column-to-column mapping. We propose to implement additional flexibility in P2DW by allowing deviations from column centers while preserving vertical structural dependencies between neighboring pixel coordinates. In order to speed up the recognition we employ hard spacial constraints on candidate alignment positions. Experiments on two well-known face datasets show that our algorithm significantly improves the recognition quality under difficult variability such as 3D rotation (poses), expressions and illuminations, and can reliably classify even automatically detected faces. We also show an improvement over state-of-the-art results while keeping computational complexity low.