This thesis focuses on facial biometrics, acquired using multi-spectral sensors, such as RGB, depth, and infrared. This data is used for authorizing users of automated and semi-automated access systems, as well as for accumulating additional information regarding facial biometrics. In this work we show that depth data, taken using an inexpensive RGB-D sensor, helps find the head pose of a subject and extract frontal-view frames. We prove experimentally that usage of the frontal-view frames improves the efficiency of face recognition. Additionally, corresponding synchronized infrared video frames allow for more efficient temperature estimation from the frontal-views. Furthermore, this thesis describes potential applications of this approach in biometric-based security systems, as well as in biomedical and health care solutions. This also shows the promising future of using biometrics in natural and contactless control interfaces.