Cross-modal sketch-photo recognition is of vital importance
in law enforcement and public security. Most existing methods
are dedicated to bridging the gap between the low-level
visual features of sketches and photo images, which is limited
due to intrinsic differences in pixel values. In this paper, based
on the intuition that sketches and photo images are highly correlated
in the semantic domain, we propose to jointly utilize
the low-level visual features and high-level facial attributes to
enhance the representation ability of sketches. More specifically,
a Multi-Modal Conditional GAN (MMC-GAN) is proposed
to generate face images for further face recognition
based on the generated images. During training, an identitypreserving
constraint is further introduced to improve the discriminative
ability of the synthetic images. Extensive experiments
demonstrate that the effectiveness of attribute-aided
face synthesis and recognition.