Résumé: In recent years, the development of deep learning has led to some advances in face synthesis approaches, but the significant pose remains one of the factors that are difficult to overcome. Benefiting from the proposal and development of the generative adversarial network, the level of face frontalization technology has reached new heights. In this paper, we propose a deep generative adversarial network based on the multi-attention mechanism for multi-pose face frontalization. Specifically, we add a deep feature encoder based on the attention mechanism and residual blocks in the generator. Meanwhile, to carry the global and local facial information, the discriminator of our model consists of four independent discriminators. The results from quantitative and qualitative experiments on CAS-PEAL-R1 dataset show that our model proves effective. The recognition of our model exceeds or equals the highest recognition rate of other models at some angles, such as 100% at ??=?0°, ??=?15° and 99.78% at ??=?30°, ??=?0°.

Langue: Anglais