This paper proposes and investigates an intelligent error-controlling scheme according to different importance of segmental information. In particular, the scheme is designed for facial images encoded by model-based coding that is a kind of intelligent compression coding. Intelligent communication systems regard the contents of information to be transmitted with extremely high compression and reliability. After highly efficient information compression by model-beaed coding, errors in the compressed information lead to severe semantic errors. The proposed scheme reduces semantic errors of information for the receiver. In this paper, we consider Action Unit (AU) as a segment of model-based coded facial image of human being and define the importance for each AU. According to the importance, an AU is encoded by an appropriated code among codes with different error-correcting capabilities. For encoding with different error controlling codes, we use three kinds of constructions to obtain unequal error protection (UEP) codes in this paper. One of them is the direct sum construction and the others are the proposed constructions which are based on joint and double coding. These UEP codes can have higher coderate than other UEP codes when minimum Hamming distance is small. By using these UEP codes, the proposed intelligent error-controlling scheme can protect information in segment in order to reduce semantic errors over a conventional error-controlling scheme in which information is uniformly protected by an error-correcting code.