This paper summarizes new research in performing face recognition using advanced correlation filters. We examine the performance of such filters in area of Biometrics for face authentication. We also compare results when applied to perform face identification. Our results are based on the illumination subsets of the CMU PIE database. We also present methods that reduce the memory requirements of these filters to run on limited computational resources including computationally efficient methods of synthesizing these filters. Finally we describe an online training algorithm implemented on a face verification system for synthesizing correlation filters from a video stream to handle pose/scale variations. This system also uses an efficient scheme to perform face localization within the current framework during the authentication stage.