A homogeneous low-resolution face recognition method using correlation features at the edge

Abstract

Face recognition technology has been well investigated in past decades and widely deployed in many real-world applications. However, low-resolution face recognition is still a challenging task in resource-constrained edge computing environment like the Internet of Video Things (IoVT) applications. For instance, low-resolution images are common in surveillance video streams, in which the rare information, variable angles, and light conditions create difficulties for recognition tasks. To address these problems, we optimized the correlation feature face recognition (CoFFaR) method and conducted experimental studies in two data preparation modes, symmetric and exhaustive arranging. The experimental results show that the CoFFaR method achieved an accuracy rate of over 82.56%, and the two-dimensional (2D) feature points after dimension reduction are uniformly distributed in a diagonal pattern. The analysis leads to the conclusion that the data augmentation advantage brought by the method of exhaustive arranging data preparation can effectively improve the performance, and the constraints by making the feature vector closer to its clustering center have no apparent improvement in the accuracy of the model identification.