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Building facial analysis systems that generalize to extreme variations inlighting and facial expressions is a challenging problem that can potentiallybe alleviated using natural-looking synthetic data. Towards that, we proposeLEGAN, a novel synthesis framework that leverages perceptual quality judgmentsfor jointly manipulating lighting and expressions in face images, withoutrequiring paired training data. LEGAN disentangles the lighting and expressionsubspaces and performs transformations in the feature space before upscaling tothe desired output image. The fidelity of the synthetic image is furtherrefined by integrating a perceptual quality estimation model, trained with faceimages rendered using multiple synthesis methods and their crowd-sourcednaturalness ratings, into the LEGAN framework as an auxiliary discriminator.Using objective metrics like FID and LPIPS, LEGAN is shown to generate higherquality face images when compared with popular GAN models like StarGAN andStarGAN-v2 for lighting and expression synthesis. We also conduct a perceptualstudy using images synthesized by LEGAN and other GAN models and show thecorrelation between our quality estimation and visual fidelity. Finally, wedemonstrate the effectiveness of LEGAN as training data augmenter forexpression recognition and face verification tasks.

LEGAN: Disentangled Manipulation of Directional Lighting and Facial Expressions by Leveraging Human Perceptual Judgements