A Systematic Literature Review of Law Enforcement Image Recognition Methods based on Generative Adversarial Networks Framework

Abstract

Image recognition research based on the Generative Adversarial Networks (GAN) has been widely used in various specialized technical fields because of its universality, adaptability, and scalability of the adversarial framework. The framework achieves the tasks of image generation, synthesis, and transformation to improve the image resolution and recognition rate, which is consistent with the purpose of the police in the image recognition work. However, the task of applying the GAN method to police image recognition is not widely explored. In this paper, we systematically review related research to understand the current applications of GAN in the field of image recognition and summarize their contributions. We analyze 35 academic papers after 2018 to provide a state-of-the-art research stream. According to the GAN applications, we divided the dataset into three domains, which are image-to-image translation, image augmentation, and mixed model. The results show that there is little difference in the number of articles in the three fields. In addition, most of the papers use image conversion methods from more than two domains, which indicates that GAN is flexible in designing the framework according to the research tasks. Based on the methods and challenges in the literature, we further propose future research directions.