An Improvement of the K-SVD Algorithm with Applications on Face Recognition

Image representation is an essential issue regarding the problems related to image processing and understanding. In the last years, the sparse representation modelling for signals has been receiving a lot of attention due to its state-of-the art performance in different computer vision tasks. One of the important factors to its success is the ability to promote representations well adapted to the data which rised with the dictionary learning algorithm. The most well known of theses algorithms is the K-SVD. In this work we proposed the αK-SVD algorithm, which tries to explore the search space of possible dictionaries better than the K-SVD. Our approach is evaluated on two public face recognition databases. The results showed that our approach achieved better results than the K-SVD and LC-KSVD when the sparsity level is low.