2D Face Recognition Based on Supervised Subspace Learning From 3D Models
Yang Li · Journal of Changchun University of Science and Technology · 2009
One of the main challenges in face recognition is represented by pose and illumination variations that drastically affect the recognition performance.This paper presents a new technique for face recognition,based on the joint use of 3D models and 2D images,specifically conceived to be robust with respect to pose and illumination changes.A 3D model of each user is exploited in the training stage to generate a large number of 2D images representing virtual views of the face with varying pose and illumination.Such images are then used to learn in a supervised manner a set of subspaces constituting the user's template.Recognition occurs by matching 2D images with the templates and no 3D information is required.