|Abstract:||Personal robotics is an area in which robot behavior is in service to few (or single) clients. This paper argues that the problems of human detection and recognition can be approached with simple yet efficient techniques that provide useful information to personal robots. By combining and taking advantage of coarse information such as motion, activities, shape, and color attributes, simple probabilistic inference algorithms can be applied to help a robot to become aware of nearby humans and their identities. Experimental results show that these simple models can be used to detect human presence robustly against a naturally clutterd and ambiguous background and perform well in a recognition test consisting of 10 subjects. Since this approach does not rely on the faces as crucial cue for detection or recognition, it can function under situations where conventional techniques would fail. Moreover, the simple model offers dramatic improvement in computation efficiency and can be used for robots to engage real-time interaction with human.