  The goal of face attribute editing is altering a facial image according to
given target attributes such as hair color, mustache, gender, etc. It belongs
to the image-to-image domain transfer problem with a set of attributes
considered as a distinctive domain. There have been some works in multi-domain
transfer problem focusing on facial attribute editing employing Generative
Adversarial Network (GAN). These methods have reported some successes but they
also result in unintended changes in facial regions - meaning the generator
alters regions unrelated to the specified attributes. To address this
unintended altering problem, we propose a novel GAN model which is designed to
edit only the parts of a face pertinent to the target attributes by the concept
of Complementary Attention Feature (CAFE). CAFE identifies the facial regions
to be transformed by considering both target attributes as well as
complementary attributes, which we define as those attributes absent in the
input facial image. In addition, we introduce a complementary feature matching
to help in training the generator for utilizing the spatial information of
attributes. Effectiveness of the proposed method is demonstrated by analysis
and comparison study with state-of-the-art methods.
