In this paper, a new approach to face recognition is presented in which not only a classifier but also a feature space of input variables is learned incrementally to adapt to incoming training samples. A benefit of this type of incremental learning is that the search for useful features and the learning of an optimal decision boundary are carried out in an online fashion. To implement this idea, an extended version of Incremental Principal Component Analysis (IPCA) and Resource Allocating Network with Long-Term Memory (RAN-LTM) are effectively combined. Using IPCA, a feature space is updated by rotating its eigen-axes and increasing the dimensions to adapt to a new training sample. In RAN-LTM, a small number of training samples called memory items are selected and they are utilized for retraining a classifier to realize an excellent incremental ability. To accommodate the classifier to the evolution of the feature space, we present a way to reconstruct the neural classifier without keeping all of the training samples given previously. In the experiments, the proposed incremental learning model is evaluated over a self-compiled face image database. As the result, we verify that the proposed model works well without serious forgetting and the test performance is improved as the learning stages proceed. © 2005 IEEE.