Survey statisticians make use of the available auxiliary information to improve estimates. One important example is given by calibration estimation, that seeks for new weights that are close (in some sense) to the basic design weights and that, at the same time, match benchmark constraints on available auxiliary information. Recently, multiple frame surveys have gained much attention and became largely used by statistical agencies and private organizations to decrease sampling costs or to reduce frame undercoverage errors that could occur with the use of only a single sampling frame. Much attention has been devoted to the introduction of different ways of combining estimates coming from the different frames. We will extend the calibration paradigm, developed so far for one frame surveys, to the estimation of the total of a variable of interest in dual frame surveys as a general tool to include auxiliary information, also available at different levels. In fact, calibration allows us to handle different types of auxiliary information and can be shown to encompass as a special cases some of the methods already proposed in the literature. The theoretical properties of the proposed class of estimators are derived and discussed, a set of simulation studies is conducted to compare the efficiency of the procedure in presence of different sets of auxiliary variables. Finally, the proposed methodology is applied to data from the Barometer of Culture of Andalusia survey.