3D face clones can be used in many areas such as Human-Computer Interaction and as preprocessing in applications, such as emotion analysis. However, such clones should be structured and the model facial shape accurately while keeping the attributes of individuals. A structured mesh is a mesh with a known semantic and topological structure. We use a face model designed from a database of 3D face examples. These global models can produce structured clones but they do not often retain the specifics of the analyzed person. Indeed, methods using models are very dependent on their databases. In our technique, we use an RGB-D sensor to get the attributes of individuals and a 3D Morphable Face Model to mark facial shape. We reverse the process classically used: we first perform fitting and then data fusion. For each depth frame, we retain the suitable data parts called Patches. This selection is performed using a distance error and the direction of the normal vectors. Depending on the location, we merge either sensor data or 3D Morphable Face Model data. We compare our method with state of the art fitting processes. The qualitative and quantitative tests show that our results are more accurate than an current fitting method and our clone has both the attributes of the person and the shape of the face well modeled.