Recognizing Minimal Facial Sketch by Generating Photorealistic Faces With the Guidance of Descriptive Attributes

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 identity-preserving 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.