Identity-Preserved Complete Face Recovering Network for Partial Face Image

Complete face recovering (CFR) is to recover the face image of a given partial face image of a target person whose photo may not be included in the gallery set. CFR has several attractive potential applications in surveillance, personal identification in forensics, to name a few, but it is challenging because of little information revealed from a single partial face image. Furthermore, the facial identity may get lost when recovering the complete face image. As far as we know, CFR problem has yet to be explored in the literature. This paper therefore proposes an identity-preserved CFR approach (IP-CFR) to tackle this problem. Accordingly, a denoising auto-encoder based network is applied. We propose an identity-preserved loss function to constrain the features in latent space of decoder, whereby maintaining the personal identity information. Then, to better restore the complete face image, the acquired features are further fed into a decoder with an adversarial structure that takes a new variant of discriminator. That is, we borrow the idea from energy based GAN that utilize an auto-encoder structure discriminator. It can produce very different gradient directions within the minibatch and therefore can make the model be trained stably. Further, we propose a novel dual-pipeline structure in the discriminator, which is leveraged to enhance the quality of the recovered image. Experimental results on the benchmark datasets show the superiority of IP-CFR.