BOOSTING STEERABLE FEATURES FOR 2D FACE RECOGNITION ON IV~2 DATABASE

摘要

In this paper, a novel approach for 2D face recognition is proposed, based on local feature extraction through a multi-resolution multi-orientation linear method, Steerable Pyramid (SP) and on a feature selection and classification by means of a non-linear method, Adaboost. Many strategies have been elaborated and tested on IV~2 database including challenging variability such as pose, expression, illumination and quality. To show the robustness of the method, it was compared to five algorithms submitted to the first evaluation campaign on 2D face recognition using IV~2 database. Proposed algorithm is almost among the two best ones.