The design of composite filters for illumination-invariant face recognition is discussed. As 3-D profiles have been extensively used as a way to ensure this behavior, the two main constraints of 3-D analysis (i.e., 3-D profile acquisition and the 3-D face-matching computational requirements) are considered. The first limitation is solved using a 3-D acquisition system based on the projection of structured color light, which provides reliable 3-D data with a very simple setup. The second shortcoming is tackled by limiting the recognition task to 2-D complexity, so the computational cost is reduced. With the 3-D profile information available, 2-D training set images that correspond to different illumination conditions are derived for the synthesis of a 2-D composite filter using the simulated annealing algorithm. The experimental results confirm the potentiality of the proposed method. Additionally, the concept of ''average face" is presented and its feasibility as an appropriate method for the recognition of a target from a set of objects is supported by experimental results.