Face Spoofing Detection in face recognition: A Review

Face spoofing detection is a critical task in the field of biometric security, with applications ranging from secure authentication systems to fraud prevention in financial and governmental sectors. Traditional methods for detecting face spoofing attacks often relied on handcrafted features and classical machine learning techniques, which were limited in their ability to capture subtle texture and motion patterns distinguishing real faces from spoofed ones. In recent years, deep learning has emerged as a powerful alternative, leveraging neural networks to automatically extract robust features from raw images or video frames. This study explores state-of-the-art deep learning techniques, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid models, to detect face spoofing attacks with high accuracy. The workflow involves preprocessing face data, feature extraction through techniques like Local Binary Patterns (LBP) or deep feature embeddings, and training deep learning models on benchmark datasets to achieve reliable spoof detection performance. .