Abstract
In this paper, we propose a novel feature extraction scheme based on the multi-resolution curvelet transform for face recognition. The obtained curvelet coefficients act as the feature set for classification, and are used to train the ensemble-based discriminant learning approach, capable of taking advantage of both the boosting and LDA (BLDA) techniques. The proposed method CV-BLDA has been extensively assessed using different databases: the ATT, YALE and FERET, Tests indicate that using curvelet-based features significantly improves the accuracy compared to standard face recognition algorithms and other multi-resolution based approaches.