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In this paper, we address the issue of face hallucination. Most current facehallucination methods rely on two-dimensional facial priors to generate highresolution face images from low resolution face images. These methods are onlycapable of assimilating global information into the generated image. Stillthere exist some inherent problems in these methods; such as, local features,subtle structural details and missing depth information in final output image.Present work proposes a Generative Adversarial Network (GAN) based novelprogressive Face Hallucination (FH) network to address these issues presentamong current methods. The generator of the proposed model comprises of FHnetwork and two sub-networks, assisting FH network to generate high resolutionimages. The first sub-network leverages on explicitly adding high frequencycomponents into the model. To explicitly encode the high frequency components,an auto encoder is proposed to generate high resolution coefficients ofDiscrete Cosine Transform (DCT). To add three dimensional parametricinformation into the network, second sub-network is proposed. This network usesa shape model of 3D Morphable Models (3DMM) to add structural constraint to theFH network. Extensive experimentation results in the paper shows that theproposed model outperforms the state-of-the-art methods.

Frequency Aware Face Hallucination Generative Adversarial Network with Semantic Structural Constraint