Human face recognition plays a significant role in security applications for access control and real time video surveillance systems, and robotics. Popular approaches for face recognition, such as principle components analysis (PCA), rely on static datasets where training is carried in a batch mode on a pre-available image set. Real world applications require that the training set be dynamic of evolving nature where new training elements can be added to the original batch without the necessity of repeating an entire batch training that includes the new elements. In this paper, various incremental PCA (IPCA) training and re-learning strategies are proposed and applied to the candid covariance-free incremental principle component algorithm. The effect of the number of increments and size of the eigen vectors on the correct rate of recognition are studied. The results suggest that batch PCA is inferior to any IPCA and that increment level relearning yields best correct recognition rate.