Machine Learning Approaches to Face Liveliness Assessment

Information Science and Engineering

In order to improve security, face recognition technology is being incorporated into biometric authentication systems more and more. However, the accuracy and dependability of these systems are seriously threatened by spoofing attacks, which use images, videos, or 3D models to mimic real people. With an emphasis on techniques intended to identify and stop such spoofing attempts, this study offers a thorough analysis of machine learning approaches to face liveliness assessment. In particular, it looks at methods such as convolutional neural networks (CNNs) and YOLO-based models for deep learning-based face identification, which are essential for differentiating between authentic and fraudulent facial representations. The paper explores a number of machine learning models, pointing out both their advantages and disadvantages for dealing with unpredictable and changing settings. Important topics are covered, including real-time detection, dataset imbalances, and performance metrics (accuracy, recall, and precision). This study also examines how these developments in machine learning strengthen face recognition systems, guaranteeing that liveliness detection can successfully distinguish real faces from fakes in practical applications.

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