Incremental PCA based face recognition

In the real world, learning is often expected to be a continuous process, which is capable of incorporating new facts into the past experience. However, currently many typical face recognition methods, such as eigenface and Fisherface, have only focused on non-incremental learning tasks, where the learning stops once the training set has been duly processed. In this paper, we present a PCA-based algorithm for face recognition, which takes the incremental learning in account. This method can update the principal subspace without simply re-computing the eigen decomposition from scratch.