A SURVEY ON FACE ANTI-SPOOFING METHODS

Abstract

Face anti-spoofing is a fundamental task in computer vision that seeks to discriminate between real and false faces. As facial recognition systems become more prevalent, it is critical to create effective methods for preventing malicious spoofing assaults. To overcome this obstacle, deep learning, a strong tool for different computer vision challenges, is being used. The application of deep learning techniques for face anti-spoofing is the emphasis of this revision.This systematic review presents an overview of current research in the topic of face anti-spoofing, with a special emphasis on the use of deep learning techniques. Face anti-spoofing prevents malicious assaults on facial recognition systems by discriminating between real and fraudulent faces. The paper begins by explaining the concept of face anti-spoofing and its importance in light of the increasing reliance on facial recognition technologies. It emphasizes the importance of robust strategies for detecting and mitigating spoofing attacks. The research then delves into the use of deep learning technologies for anti-spoofing of faces. It analyzes the capacity of several deep learning models, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), to learn discriminative features from raw facial data. The research investigates various tactics used to train these models, such as data augmentation approaches and transfer learning, in order to improve their performance and generalization capabilities. We end our analysis by outlining current open challenges and possible opportunities.