Multi-biometrics, or the fusion of more than one biometric modality, sample, sensor, or algorithm, is quickly gaining popularity as a method of improving biometric system performance and robustness. Despite the recent growth in multibiometrics research, little investigation has been done to explore the possibility of achieving multi-modal fusion from a single sensor. This approach to multi-biometrics has numerous advantages, including the potential for increased recognition rates, while still minimizing sensor cost and acquisition times. In this work, experiments are presented which successfully combine multiple samples of face and iris biometrics obtained from a single stand-off and on-the-move video sensor. Several fusion techniques are explored, with the best recognition rates achieved by using a weighted summation of face and iris match scores. The fusion results out-perform either single-modality approach, and the proposed multi-biometric framework represents a viable and natural extension to the stand-off iris sensor used to acquire subject data.