The lack of a common benchmark for the evaluation of the gaze estimation task from RGB and RGB-D data is a serious limitation for distinguishing the advantages and disadvantages of the many proposed algorithms found in the literature. The EYEDIAP database intends to overcome this limitation by providing a common framework for the training and evaluation of gaze estimation approaches. In particular, this database has been designed to enable the evaluation of the robustness of algorithms with respect to the main challenges associated to this task: i) Head pose variations; ii) Person variation; iii) Changes in ambient and sensing conditions and iv) Types of target: screen or 3D object. This technical report contains an extended description of the database, we include the processing methodology for the elements provided along with the raw data, the database organization and additional benchmarks we consider relevant to evaluate diverse properties of a given gaze estimator.