Cooperative Orientation Generative Adversarial Network for Latent Fingerprint Enhancement

Robust fingerprint enhancement algorithm is crucial to latent fingerprint recognition. In this paper, a latent fingerprint enhancement model named cooperative orientation generative adversarial network (COOGAN) is proposed. We formulate fingerprint enhancement as an image-to-image translation problem with deep generative adversarial network (GAN) and introduce orientation constraints to it. The deep architecture provides a powerful representation for the translation between latent fingerprint space and enhanced fingerprint space. While the orientation supervision can guide the deep feature learning to focus more on the ridge flows. To further boost the performance, a quality estimation module is proposed to remove the unrecoverable regions while enhancement. Experimental results show that COOGAN achieves state-of-the-art performance on NIST SD27 latent fingerprint database.