Complete Face Recovering: An Approach towards Recognizing a Person by a
Single Partial Face Image without the Target Photo in Gallery
Abstract
Complete face recovering (CFR) is to recover the complete face image of
a given partial face image of a target person whose photo may not be
included in the gallery set. The CFR has several attractive potential
applications but is challenging. As far as we know, the CFR problem has
yet to be explored in the literature. This paper therefore proposes an
identity-preserved CFR approach (IP-CFR) to addressing the CFR. First, a
denoising auto-encoder based network is applied to acquire the
discriminative feature. Then, we propose an identity-preserved loss
function to keep the personal identity information. Furthermore, the
acquired features are fed into a new variant of the generative
adversarial network (GAN) to restore the complete face image. In
addition, a two-pathway discriminator is leveraged to enhance the
quality of the recovered image. Experimental results on the benchmark
datasets show the promising result of the proposed approach.