Transfer deep feature learning for face sketch recognition

Sketch-to-photo recognition is an important challenge in face recognition because it requires matching face images in different domains. Even the deep learning, which has recently been deployed in face recognition, is not efficient for face sketch recognition due to the limited sketch datasets. In this paper, we propose a novel face sketch recognition approach based on transfer learning. We design a three-channel convolutional neural network architecture in which the triplet loss is adopted in order to learn discriminative features and reduce intra-class variations. Moreover, we propose a hard triplet sample selection strategy to augment the number of training samples and avoid slow convergence. With the proposed method, facial features from digital photos and from sketches taken from the same person are closer; the opposite occurs if the digital photo and sketch are from different identities. Experimental results on multiple public datasets indicate that the proposed face sketch recognition method outperforms the existing approaches.