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The ability to recognize people is a key element for improving human-robot interaction in service robots. There are many approaches for face recognition; however, these assume unrealistic conditions for a service robot, like having an image with a centered face under controlled illumination. We have developed a novel face recognition system so that a mobile robot can learn new faces and recognize them in real-time in realistic indoor environments. It is able to learn online a new face based on a single frame, which is later used to recognize the person even under different environmental conditions. We employ a preprocessing step to reduce the effect of different illumination conditions, and then identify 3 regions in the face: left eye, right eye and nose-mouth. SIFT features are extracted from each region and stored in a feature vector, which is used for recognition. The matching strategy is able to discard unknown faces and the recognition process uses a Bayesian approach over several frames to improve accuracy. Experimental results in natural environment and with Yale's face database show that that our approach is able to learn different faces from a single image and recognize them on average in three seconds, with very competitive results.