For human-robot interaction users have to be robustly identified and their appearances learned online. Existing state of the art methods for face recognition do not support online learning of faces and lack the recognition performance required to be used in real-world situations. Hence a novel method is introduced in this paper as a descriptor, which provides the required performance by increasing the separability of the classes by maximizing the inter-class and minimizing intra-class variations. The robustness against variations in lighting and pose as well as the speed is increased by selecting only the most representative samples. Additionally to allow for classification of unknown faces, a novel method has been introduced. The main benefit over the state of the art methods is finding the relation between the distance of classification and the certainty of that classification. This relation is automatically calculated from the data belonging to each class. In that way novelty detection can be performed. To further improve recognition performance a method has been used that utilizes multiple frames in classification. To prove the benefits of the introduced methods extensive experiments have been performed on a state of the art face recognition database.