Face photo-sketch recognition based on multi-directional line features projection

Face photo-sketch recognition plays an important role in law enforcement, particularly in narrowing down the search for potential suspects based on limited sketch information. However, the issues of large modality gap and having a relatively small number of sketch samples for training remained a challenging task. In this paper, we propose a novel feature descriptor network for automated face photo-sketch recognition that is suitable for modality discrepancy and small dataset learning. By stacking a multi-directional image difference operation over a pooling projection in a multilayer fashion, our proposal forms an interpretable learning system that does not show obvious overfitting on limited training data. Extensive evaluation using three public face photo-sketch databases shows competing rank-1 recognition accuracy of the proposed method comparing with state-of-the-art methods. In terms of average ranking on the three experimented databases, the proposed method has the top average rank of 2 among 17 algorithms with the runner-up LFDA algorithm having an average rank of 2.83.