Learning-based Face Synthesis for Pose-Robust Recognition from Single Image

Face recognition in real-world conditions requires the ability to deal with a number
of conditions, such as variations in pose, illumination and expression. In this paper, we
focus on variations in head pose and use a computationally efficient regression-based
approach for synthesising face images in different poses, which are used to extend the
face recognition training set. In this data-driven approach, the correspondences between
facial landmark points in frontal and non-frontal views are learnt offline from manually
annotated training data via Gaussian Process Regression. We then use this learner to
synthesise non-frontal face images from any unseen frontal image. To demonstrate the
utility of this approach, two frontal face recognition systems (the commonly used PCA
and the recent Multi-Region Histograms) are augmented with synthesised non-frontal
views for each person. This synthesis and augmentation approach is experimentally
validated on the FERET dataset, showing a considerable improvement in recognition
rates for ±40◦ and ±60◦ views, while maintaining high recognition rates for ±15◦
and ±25◦ views.