Cherry Ali, Nasser Aya, Salameh Wassim, Abou Ali Mohamad, Hajj-Hassan Mohamad
Department of Biomedical Engineering, Lebanese International University, Beirut P.O. Box 146404, Lebanon.
Department of Biomedical Engineering, International University of Beirut, Beirut P.O. Box 146404, Lebanon.
Sensors (Basel). 2024 Dec 25;25(1):40. doi: 10.3390/s25010040.
The integration of liveness detection into biometric systems is crucial for countering spoofing attacks and enhancing security. This study investigates the efficacy of photoplethysmography (PPG) signals, which offer distinct advantages over traditional biometric techniques. PPG signals are non-invasive, inherently contain liveness information that is highly resistant to spoofing, and are cost-efficient, making them a superior alternative for biometric authentication. A comprehensive protocol was established to collect PPG signals from 40 subjects using a custom-built acquisition system. These signals were then transformed into two-dimensional representations through the Gram matrix conversion technique. To analyze and authenticate users, we employed an EfficientNetV2 B0 model integrated with a Long Short-Term Memory (LSTM) network, achieving a remarkable 99% accuracy on the test set. Additionally, the model demonstrated outstanding precision, recall, and F1 scores. The refined model was further validated in real-time identification scenarios, underscoring its effectiveness and robustness for next-generation biometric recognition systems.
将活体检测集成到生物识别系统中对于对抗欺骗攻击和增强安全性至关重要。本研究调查了光电容积脉搏波描记法（PPG）信号的有效性，该信号相对于传统生物识别技术具有明显优势。PPG信号是非侵入性的，本质上包含对欺骗具有高度抗性的活体信息，并且具有成本效益，使其成为生物识别认证的优越替代方案。建立了一个综合协议，使用定制的采集系统从40名受试者收集PPG信号。然后通过Gram矩阵转换技术将这些信号转换为二维表示。为了分析和认证用户，我们采用了与长短期记忆（LSTM）网络集成的EfficientNetV2 B0模型，在测试集上达到了99%的显著准确率。此外，该模型还展示了出色的精确率、召回率和F1分数。优化后的模型在实时识别场景中进一步得到验证，突出了其对下一代生物识别系统的有效性和鲁棒性。
