A RELATION NETWORK EMBEDDED WITH PRIOR FEATURES FOR FEW-SHOT CARICATURE RECOGNITION

Caricature is a simple and abstract description of a person using her/his exaggerated characteristics. Due to amplified facial variations in the caricatures and significant differences among caricature and real face modalities, building vision models for recognizing each other between these modalities is an extremely challenging task. In addition, it is not easy to collect abundant samples of real faces and corresponding caricatures for training vision models, which makes the recognition more difficult. In this paper, we propose a novel relation network via meta learning to address the problem of few-shot caricature face recognition. In particular, we present a deep relation network to capture and memorize the relation among different samples. To employ the prior knowledge, we combine learned deep and handcrafted features to form the hybrid-prior representation via joint meta learning. Final recognition is derived from our relation network by learning to compare between the hybrid-prior features of samples. Experimental results on three caricature datasets of Web-Caricature, IIIT-CFW, and Caricature-207 demonstrate that our method performs better than many existing ones for few-shot caricature recognition.