Enterprise AI Analysis
TL-Efficient-SE: A Transfer Learning-Based Attention-Enhanced Model for Fingerprint Liveness Detection Across Multi-Sensor Spoof Attacks
This paper introduces TL-Efficient-SE, a deep learning framework combining EfficientNetB0 with Squeeze-and-Excitation attention for robust fingerprint liveness detection. It achieves high accuracy across various sensors and spoof materials, addressing critical security needs in biometric systems.
Executive Impact & Strategic Value
TL-Efficient-SE employs transfer learning with a pre-trained EfficientNetB0 model for enhanced feature extraction and integrates a Squeeze-and-Excitation (SE) attention mechanism to improve feature discrimination by adaptively recalibrating channel-wise feature maps. The model was trained and tested on the LivDet 2015 dataset, demonstrating superior performance with accuracies between 98.50% and 99.50% and AUCs from 0.978 to 0.9995 across multiple optical sensors (Green Bit, CrossMatch, HiScan) and spoof materials (PlayDoh, Ecoflex, Gelatine). This framework offers a robust, efficient, and generalizable solution for real-time biometric security against sophisticated presentation attacks, significantly outperforming existing methods like Slim-ResCNN and HyFiPAD.
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Deep Analysis & Enterprise Applications
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Enterprise Process Flow
Data Preprocessing
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Feature Extraction (EfficientNetB0)
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SE Attention Mechanism
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Fully Connected Layers & Classification
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Training & Evaluation
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99.50%
Accuracy on Green Bit Sensor
The model achieved top-tier performance on the Green Bit sensor, demonstrating exceptional precision in live/spoof differentiation, highlighting its ability to capture fine-grained texture and material differences.
Application in Secure Biometric Systems
The TL-Efficient-SE model provides a robust and efficient solution for protecting against sophisticated spoofing attacks in real-world scenarios. Its exceptional performance and generalization capabilities make it highly suitable for applications in mobile authentication and secure access control. This framework ensures that genuine users are not erroneously rejected (low false negatives) while effectively thwarting presentation attacks across diverse sensor types and spoof materials, significantly enhancing the dependability of biometric systems. Future work will focus on sensor-invariant learning and adaptive thresholds for even greater resilience, aligning with industry standards like ISO/IEC 30107.
Application in Secure Biometric Systems
The TL-Efficient-SE model provides a robust and efficient solution for protecting against sophisticated spoofing attacks in real-world scenarios. Its exceptional performance and generalization capabilities make it highly suitable for applications in mobile authentication and secure access control. This framework ensures that genuine users are not erroneously rejected (low false negatives) while effectively thwarting presentation attacks across diverse sensor types and spoof materials, significantly enhancing the dependability of biometric systems. Future work will focus on sensor-invariant learning and adaptive thresholds for even greater resilience, aligning with industry standards like ISO/IEC 30107.
This model directly addresses critical security vulnerabilities in biometric systems by providing a highly robust and generalized solution for presentation attack detection, ensuring enhanced trust and integrity.
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Implementation Roadmap
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Expand the AI solution across the enterprise, implement advanced security measures, and establish a framework for ongoing innovation and adaptation.
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