Unconstrained low-resolution face recognition is a prominent area for researchers in computer vision. Due to the unavailability of abundant visual features, it is hard to improve the identification rate in low-resolution (LR) images. To boost the identification rate on LR images, we have proposed an attention network that internally consists of a local and global attention module. The local attention module focuses on the early features, while the global attention module focuses on deep features inside the network. To make up for the deficit of resolution aspects aware images, we have also generated multi-resolution images from high-resolution (HR) images where resolution aspects are covered. Experimenting on the TinyFace dataset, we have achieved a \(54.1\%\) identification rate with a margin of \(6.2\%\) . Furthermore, a train-test pipeline is also implemented to boost the identification rate on super-resolved tiny faces where state-of-the-art (SOTA) models fail. After applying the super-resolution (SR) approaches on tiny faces, the identification rate reaches \(54.4\%\) with a large margin of \(6.5\%\) . The identification rate achieved on the Surveillance Cameras Face (SCface) Database is \(80.0\%\) with a giant margin of \(12.7\%\) at a distance of 4.2 m. Heat maps are also visualized to locate the key discriminant areas in unconstrained LR images to witness the effectiveness of the attention network. A detailed quantitative experimental analysis is performed to produce qualitative results comparable to SOTA models.