Face pose reconstruction based on multitask detail compensated generative adversarial networks

Abstract

Face pose variation is easy to cause feature loss and face recognition rate decline, which is a key problem in the field of face image generation and recognition. Existing face generation methods based on encoder-decoder often focus on pose conversion and lose facial detail features. This paper proposes a Multitask Detail Compensated Generative Adversarial Networks (MDC-GAN), which improves the effect of generating face details and preserving identity through multi-task learning and multi-scale feature fusion. It has achieved good face reconstruction results on the FERET database, and the results are better than other current methods.