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    • 摘要: 光学指纹活体检测在防止伪造指纹攻击指纹识别系统中发挥重要作用。已有的基于深度学习的指纹活体检测方法依赖大量标注数据,而指纹图像存在采集困难的问题。针对小样本场景,提出一种基于空域和频域特征比对的光学指纹活体检测方法。该方法通过双向双域交叉注意力机制和高频增强因子提升小样本场景下的活体检测性能。实验结果表明,提出的方法在两个基准数据集上均表现优异,在仅有10个样本时,平均分类误差(ACER)分别低至0.21%和0.45%,优于现有方法。此外,提出的方法在跨传感器检测中也展现了良好的适应性。该方法拓展了指纹活体检测技术的实际应用场景。

       

      Abstract: Optical fingerprint liveness detection plays a crucial role in preventing spoofing attacks on fingerprint recognition systems. Existing deep learning-based methods require large amounts of labeled data, while fingerprint image acquisition remains challenging. A few-shot optical fingerprint liveness detection method, featuring the fusion of spatial and frequency domain features, has been proposed for few-shot scenarios. Performance in liveness detection under few-shot conditions is enhanced using a bidirectional cross-domain attention mechanism and a high-frequency enhancement factor. Experimental results demonstrate outstanding performance on two benchmark datasets. With only 10 samples, the average classification error rate (ACER) reaches as low as 0.21% and 0.45%, outperforming existing methods. Notably, excellent adaptability in cross-sensor detection is shown. The method expands the practical applications of fingerprint liveness detection technology.