Synthetic Data Approach for Unconstrained Low-Resolution Face Recognition in Surveillance Applications✱

Abstract

Unconstrained low-resolution (LR) face recognition is still a challenging problem in computer vision. In real-world scenarios, the gallery images are generally of high-resolution (HR), while the probe images may be of low resolution. In surveillance applications, the challenge of matching LR to HR is more common because the probe images are captured in low resolution while gallery images are of high resolution. LR to HR face matching is challenging because, in the embedding space, there is a need for a common subspace for mapping the LR and HR embeddings. In LR to LR face matching, where probe and gallery both belong to low-resolution, face identification is more difficult because very less visual information is present in the images. In LR to LR face matching, the challenge becomes very hard if faces are tiny in size and belong to low-resolution. In this paper, we implement a deep learning pipeline for matching the LR to HR and LR to LR faces. Due to the absence of LR and HR images of the same identity in the real-world datasets, we have also generated the LR images from HR images using the synthetic data approach. Extensive experimental analyses have been made to compare the performance to other state-of-the-art models.