Incremental maximum margin criterion based on eigenvalue decomposition updating algorithm
Dimensionality reduction has been proven to be a critical data processing step for face recognition. Maximum margin criterion (MMC) is one of the popular supervised dimensionality reduction algorithms. However, the original implementation of MMC is not suitable for incremental learning problem. In this paper, we first propose an eigenvalue decomposition updating algorithm (EVDU) for symmetric matrix. Then, based on our proposed EVDU technique, we propose an incremental MMC (EVDU-IMMC) method which can update the discriminant vectors of MMC when new samples are inserted into the training set. Experiments on ORL and PIE face databases show that the proposed EVDU-IMMC gives the same performance as the batch MMC with much lower computational complexity. The experimental results also show that our proposed EVDU-IMMC gives better performance than other IMMC method in terms of recognition accuracy and computational efficiency.
dimensionality reduction face recognition incremental learning feature extraction small sample size problem