Today, fingerprint recognition technology has aroused wide attention in the society, especially in the application of identity authentication with a smartphone as a carrier. However, the disadvantage of these devices is that the identification sensors are vulnerable to spoofing attacks from artificial replicas made from clay, gelatin, silicon, etc. To resolve it, a feasible anti-deception countermeasure, called fingerprint liveness detection (FLD), has been proposed. Different from most shallow feature methods, the deep convolutional neural network (DCNN)-based FLD methods have been widely explored with the properties of fast operation, few parameters, and end-to-end feature self-learning. Meanwhile, DCNN faces a pair of contradictory problems, on the one hand, the training accuracy will keep rising with the increasement of multilayer perceptron (MLP), finally tends to a stable value. Continue to increase the number of MLP, results will decline. Much research, on the other hand, shows that the number of MLP is the foundation for realizing a high performance detection. Hereby, we apply deep residual network (DRN) to FLD for the first time to solve the contradiction mentioned in this paper. Next, to eliminate the interference of invalid regions of given images, a region-of-interest (ROI) extraction algorithm is put forward. Afterward, to avoid the parameters learned plunging into local optimization, adaptive learning-based DRNs (ALDRNs), which automatically adjust the learning rate if those monitoring parameters (verification accuracy) are stable, are explored. Finally, we propose a novel texture enhancement based on the local gradient pattern (LGP) method to improve the generalization of a model classifier as well. Experimental results on three benchmark data sets: LivDet 2011, 2013, and 2015, show that our results outperform the state-of-the-art FLD methods.

中文翻译：

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用于指纹活体检测的具有自适应学习框架的深度残差网络

如今，指纹识别技术已引起社会广泛关注，尤其是在以智能手机为载体的身份认证应用中。然而，这些设备的缺点是识别传感器容易受到由粘土、明胶、硅等制成的人造复制品的欺骗攻击。为了解决这个问题，一种可行的反欺骗对策，称为指纹活体检测 (FLD)，被提出。与大多数浅层特征方法不同，基于深度卷积神经网络 (DCNN) 的 FLD 方法具有运算速度快、参数少、端到端特征自学习等特点，得到了广泛的探索。同时，DCNN 面临着一对矛盾的问题，一方面，随着多层感知器（MLP）的增加，训练精度会不断提高，最终趋于稳定值。继续增加MLP的数量，结果会下降。另一方面，大量研究表明，MLP 的数量是实现高性能检测的基础。在此，我们首次将深度残差网络（DRN）应用于 FLD 以解决本文中提到的矛盾。其次，为了消除给定图像无效区域的干扰，提出了一种感兴趣区域（ROI）提取算法。之后，为了避免学习到的参数陷入局部优化，探索了基于自适应学习的 DRN（ALDRN），如果这些监控参数（验证精度）稳定，它会自动调整学习率。最后，我们提出了一种基于局部梯度模式（LGP）方法的新型纹理增强，以提高模型分类器的泛化能力。在三个基准数据集上的实验结果：LivDet 2011、2013 和 2015，表明我们的结果优于最先进的 FLD 方法。