UC Berkeley

Mooney faces are special two-tone images that elicit a rich impression of identity and facial expression in human observers. While Mooney faces are important, there exist only a small number of instances hand-crafted from source photos which are often no longer available. We first apply deep learning methods to generate a plausible Mooney face automatically from any face photo. We are then able to create a large-scale face dataset with paired grayscale and two-tone images. We then study how well two-tone versions make face predictions, using conditional Generative Adversarial Networks. We show that faces predicted from Mooney images bear striking resemblance to source photos, and they are better than two-tone images obtained by global intensity thresholding. We also demonstrate remarkable face predictions from very low resolution surveillance photos. Our findings reveal great potentials of combining deep learning and Mooney faces for more effective face recognition in a wide range of conditions.