Abstract: Illumination variation significantly affects the performance of face recognition algorithms. However, it is difficult to construct a database that meets the demand of illumination analysis with current face image database construction technology. The influence of illumination variation on deep face recognition based on 3D face models is investigated. First a new method is presented to produce face images under arbitrary illuminations by using a 3D face model based on face basis image representation theory proposed. Then the database used for illumination analysis is constructed. Afterwards, an optimal way is explored to select illuminations used for constructing a high quality face image database by analyzing the impacts of different illumination sampling strategies on the performances of face recognition models. With the advantage of accurate illumination labeling of virtual data, the performances of different illumination labeling methods on the training of face recognition network are tested on a multi-task learning framework. Through experiments on the effects of different lighting sampling schemes and labeling methods on the performances of face recognition models carried out on virtual data and real data, it is concluded that using an appropriate amount of lightings of basis images to build a database is an effective lighting sampling scheme, and accurate lighting annotation can help further improve the face recognition rate, the corresponding recognition models have face recognition accuracy rates of more than 98% on virtual and real monitoring datasets which contain face images captured under very challenging lighting conditions. Proposed work improve the performance of deep face recognition models, and facilitate the construction of high quality face image databases with various illuminations.