GeoDNN: Geometry-Aware Deep Neural Networks for Cross-Domain Fingerprint Spoof Detection
Keywords:
Anomaly Detection, Behavioral Representation, Biometric Authentication, Deep Learning, Fraud Detection, Neural NetworksAbstract
Fingerprint-based biometric authentication remains a cornerstone of modern security. Cross-domain fingerprint spoof detection remains a critical challenge in sophisticated biometric authentication systems due to variations introduced by heterogeneous sensors, acquisition conditions, and spoof fabrication materials. Conventional spoof detection methods often rely on texture-based or sensor-specific handcrafted features, which exhibit limited generalization when deployed across unseen domains. To address this limitation, this paper proposes GeoDNN, a Geometry-Aware Deep Neural Network framework designed to learn intrinsic spatial and structural representations of fingerprint patterns for robust cross-domain spoof detection. GeoDNN explicitly models geometric ridge-valley structures and spatial consistency cues through deep hierarchical feature learning, reducing dependence on domain-specific artifacts. The framework integrates deep neural architectures and anomaly-aware discrimination to enhance robustness against class imbalance and previously unseen spoof types. Extensive evaluation across multiple large-scale fingerprint spoof datasets demonstrates that GeoDNN achieves an average detection accuracy of 96.2%, a true detection rate of 93.8%, and a precision of 91.5%, outperforming state-of-the-art methods by up to 2.8% in accuracy and 4.2% in detection rate. False acceptance rates remain below 5.2% across domains, confirming strong generalization capability. The results validate that geometry-aware deep representations significantly enhance cross-domain fingerprint spoof detection and provide a scalable solution for secure biometric authentication systems.
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