FINGERPRINT LIVENESS DETECTION VIA GLOBAL FEATURE ENCODING WITH VISION TRANSFORMERS

Fingerprint Liveness Detection (FLD) is crucial for securing biometric systems by identifying fake fingerprints made from materials like silicone, gelatine, and latex. To reduce complexity and computational cost, a lightweight deep learning framework is proposed that efficiently captures both fine ridge-level details and global texture features. The system relies solely on fingerprint images, ensuring hardware simplicity and easy deployment without additional biometric inputs. It uses an optimized architecture with advanced training techniques to improve accuracy and performance. As a result, the model achieves strong generalization and reliable performance in real-world applications.