Recently, we experienced significant advancement in intelligent service robots. The remarkable features of an intelligent robot include tracking and identification of person using biometric features. The human-robot interaction is very important because it is one of the final goals of an intelligent service robot. Many researches are concentrating in two fields. One is self navigation of a mobile robot and the other is human-robot interaction in natural environment. In this paper we will present an effective person identification method for HRI (Human Robot Interaction) using two different types of expert systems. However, most of mobile robots run under uncontrolled and complicated environment. It means that face and speech information can't be guaranteed under varying conditions, such as lighting, noisy sound, orientation of a robot. According to a value of illumination and signal to noise ratio around mobile a robot, our proposed fuzzy rule make a reasonable person identification result. Two embedded HMM (Hidden Marhov Model) are used for each visual and audio modality to identify person. The performance of our proposed system and experimental results are compared with single modality identification and simply mixed method of two modality.