Fingerprint Intrusion Detection System

Fingerprint intrusion detection systems aim to safeguard fingerprint authentication systems against presentation attacks—attempts to spoof a fingerprint using artificial replicas. Current research heavily emphasizes deep learning approaches, particularly convolutional neural networks like MobileNet, and refined algorithms such as Faster R-CNN variants, to improve the accuracy and robustness of these systems across various attack types and sensor modalities. This field is crucial for enhancing the security of biometric authentication in applications ranging from smartphone access to high-security access control, driving ongoing efforts to develop more accurate and resilient detection methods.