Face Recognition is the process of identifying people based on facial images. In its most usual form the identification procedure consists of extracting information from an input face image and comparing them to the records of other face images stored in a face data base, and finally indicating the most similar one to the input image. The performance of this process is directly dependent on the input image quality, as well as on the images in the data base. Thus, it is important that the quality of a face image is tested before it is given to the recognition procedure, either as a input image or as a new record in the face database. Most methods proposed thus far based on a set of criteria, each one devoted to an isolated attribute. The image quality is considered adequate if approved by all criteria individually. Thus, the cumulative effect of different factors affecting the image quality is no regarded. This dissertation proposes a methodology for the design of quality metrics of facial images that Express in a single scalar the combined effect of multiple factors affecting the quality. Such score is given by a function of attributes extracted directly from the image. This study investigates a linear and a non-linear approach for quality assessment. Experiments conducted upon the IMM face database for a Local Binary Pattern face recognition algorithm demonstrate the good performance of the proposed methodology.