Abstract
This paper presents an approach called Gabor-Feature-Based Local Generic Representation (G-LGR), which take advantages of the sparse representation properties of face recognition in biometric applications. In this work, the main problem is that if only one training subject per class is available. One of the novelties of our new algorithm is to produce virtual samples of each subject; the new sample generic of a gallery set is used in order to generate the intra-personal variations of different individuals. We compare our approach against different state-of-the-art techniques using the AR face database.