Incremental complete LDA for face recognition

The complete linear discriminant analysis (CLDA) algorithm has been proven to be an effective tool for face recognition. The CLDA method can make full use of the discriminant information of the training samples. However, the original implementation of CLDA may not suitable for incremental learning problem. In this paper, we first propose a new implementation of CLDA, which is theoretically equivalent to the original implementation of CLDA but is more efficient than the original one. Then, based on our proposed novel implementation of CLDA, we propose the incremental CLDA method which can accurately update the discriminant vectors of CLDA when new samples are inserted into the training set. Experiments on ORL, AR and PIE face databases show the efficiency of our proposed CLDA algorithms over the original implementation of CLDA.

► We propose a new implementation of complete linear discriminant analysis (CLDA). ► New implementation of CLDA is theoretically equivalent to the original one. ► New implementation of CLDA is more efficient than the original one. ► Based on the new implementation of CLDA, we propose the incremental CLDA (ICLDA). ► ICLDA can accurately update the discriminant vectors of CLDA when new samples are added.