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Assessing Transfer Learning on Convolutional Neural Networks for Patch-Based Fingerprint Liveness Detection

机译：评估卷积神经网络上基于补丁的指纹活度检测的转移学习

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摘要

Fingerprint based biometric identification systems are vulnerable to spoofing attacks that involve the use of fake replicas of real fingerprints. The resulting security issues can be mitigated through the development of software modules capable of detecting the liveness of an input image and, thus, of discarding fake fingerprints before the classification step. In this work we present a fingerprint liveness detection method that combines a patch-based voting approach with Transfer Learning techniques. Fingerprint images are first segmented to discard background information. Then, small-sized foreground patches are extracted and processed by popular Convolutional Neural Network models, whose pre-trained versions were adapted to the problem at hand. Finally, the individual patch scores are combined to obtain the fingerprint label. Experimental results on well-established benchmarks show the promising performance of the proposed method compared with several state-of-the-art algorithms.
机译：基于指纹的生物特征识别系统很容易遭受欺骗攻击，这些欺骗攻击涉及使用真实指纹的伪造副本。通过开发能够检测输入图像的真实性并因此能够在分类步骤之前丢弃假指纹的软件模块，可以缓解由此产生的安全问题。在这项工作中，我们提出了一种指纹活跃度检测方法，该方法将基于补丁的投票方法与转移学习技术相结合。指纹图像首先被分割以丢弃背景信息。然后，通过流行的卷积神经网络模型提取和处理小型前景补丁，该模型的预训练版本已适应当前的问题。最终，将各个补丁得分相结合以获得指纹标签。在完善的基准上的实验结果表明，与几种最新算法相比，该方法具有令人鼓舞的性能。

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