DISGUISED FACIAL RECOGNITION USING NEURAL NETWORKS - A SURVEY

Face recognition software is being used more often in a wide range of applications, but worries about privacy and exploitation have sparked interest in creating methods for “disguised” face identification. The use of neural networks for disguised face identification has recently attracted the attention of academics, who trained their algorithms on pictures of people with masks, hats, and other facial coverings. Yet, there are a number of difficulties in creating efficient disguised facial recognition systems, such as a lack of training data and the requirement to take different lighting and position changes into consideration. The use of adversarial networks, infrared imaging, and deep learning techniques are among the latest advancements in the field of masked facial recognition using neural networks that are examined in this review. The study also covers prospective uses for concealed face recognition technology as well as the moral issues surrounding its implementation. The survey’s findings show that neural networks may be used to mask face recognition in order to satisfy privacy concerns while still enabling accurate identification of people in a range of scenarios.