Face Recognition (FR) using Convolutional Neural Network (CNN) based models have achieved considerable success in constrained environments. They however fail to perform well in unconstrained scenarios, especially when the images are captured using surveillance cameras. These probe samples suffer from degradations such as noise, poor illumination, low resolution, blur as well as aliasing, when compared to the rich training (gallery) set, comprising mostly of mugshot images captured in laboratory settings. These images in the training (gallery) set are crisp and have high contrast, compared to the probe samples. To cope with this scenario, we propose a novel dual-pathway generative adversarial network (DP-GAN) which maps low resolution images captured using surveillance camera into their corresponding high resolution images, which are gallery-like, using a novel combination of multi-scale reconstruction and Jensen-Shannon divergence based loss. These images thus obtained are then used to train a deep domain adaptation (deep-DA) network to perform the task of FR. The proposed network achieves superior results (>90%) on four benchmark surveillance face datasets, evident from the rank-1 recognition rates when compared with recent state-of-the-art CNN-based techniques.