Liveness Detection

Liveness detection aims to distinguish genuine biometric data (e.g., fingerprints, faces) from spoofed imitations, safeguarding biometric authentication systems against attacks. Current research heavily emphasizes improving the generalization of liveness detection models across diverse datasets and attack types, employing deep learning architectures like Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), often incorporating techniques such as contrastive learning and domain adaptation. These advancements are crucial for enhancing the security and reliability of biometric systems in various applications, from financial transactions to access control, by mitigating the vulnerability to increasingly sophisticated spoofing methods. The field also actively explores multimodal approaches and novel feature extraction techniques to improve robustness and accuracy.