Low-resolution face recognition: Review, challenges and research directions

Abstract

Low-resolution face recognition (LRFR) is an active research area as it is widely used in forensics and surveillance systems. A lot of effort has been put into improving the performance of the system since its inception. Recent deep neural network models have demonstrated outstanding face recognition performance on various face data sets with challenges like variations in pose, illumination, and occlusion and surpassed the performance of humans in these tasks. But, the accuracy of the LRFR method is still a problem. There is no fixed definition for considering any image as a low-resolution (LR) image. Most of the researchers have considered the image below 32 × 32 as a low-resolution image. This paper discusses various methods and algorithms used in improving the performance of low-resolution face recognition (LRFR). We have presented a thorough study of all the processes included in face recognition tasks including face detection, feature mapping, super-resolution, and face recognition. The study includes methodology along with the dataset and performance measures. We have also summarized the study of different datasets used in LRFR along with various source codes used to perform experimentation on LRFR. We have also presented a study in terms of accuracy of different LRFR methods on different dataset. Finally, challenges and research directions are presented to further carry out the LRFR research.