Fingerprints are widely used for biometric recognition. However, many spoofing attacks based on an artificially made fingerprint occur. In this study, the authors propose an approach to detect fingerprint liveness which uses the guided filtering and hybrid image analysis. This study deals with the problem of ignoring the contribution that is brought by the sharp features when analysing the denoised image. The method described utilises both the enhanced sharp features and denoised features from the hybrid images to get better results. The input fingerprint is pre-processed by region of interest extraction and then is filtered by a guidance image for obtaining the denoised image. Then, histogram equalisation is introduced to eliminate the impact of illumination condition. The authors extract the co-occurrence of adjacent local binary pattern features from both the cropped images and the denoised images. Whilst concatenating both the features together to form a long feature, t-Distributed Stochastic Neighbour Embedding is applied to reduce the data dimension. The authors consider the fingerprint liveness detection as a two-class classification problem and use support vector machine with radial basis function kernel to solve this problem. The authors evaluate the experiments on three benchmark data sets. Experimental results demonstrate that the accuracy of the proposed method can outperform most of the state-of-art methods.

中文翻译：

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基于引导滤波和混合图像分析的指纹活度检测

指纹被广泛用于生物特征识别。但是，基于人工制造的指纹的许多欺骗攻击都会发生。在这项研究中，作者提出了一种使用引导滤波和混合图像分析的指纹活度检测方法。这项研究解决了在分析去噪图像时忽略尖锐特征带来的贡献的问题。所描述的方法利用混合图像中增强的锐利特征和去噪特征来获得更好的结果。通过感兴趣区域提取对输入指纹进行预处理，然后通过引导图像​​对其进行滤波以获得去噪图像。然后，引入直方图均衡化以消除照明条件的影响。作者从裁剪后的图像和去噪后的图像中提取相邻的局部二进制图案特征的共现。在将两个要素连接在一起以形成长要素的同时，应用t分布随机邻居嵌入来减小数据尺寸。作者将指纹活度检测视为两类分类问题，并使用带有径向基函数核的支持向量机来解决此问题。作者评估了三个基准数据集上的实验。实验结果表明，所提方法的准确性优于大多数最新方法。使用t分布随机邻居嵌入可减少数据量。作者将指纹活度检测视为两类分类问题，并使用带有径向基函数核的支持向量机来解决此问题。作者评估了三个基准数据集上的实验。实验结果表明，所提方法的准确性优于大多数最新方法。使用t分布随机邻居嵌入可减少数据量。作者将指纹活度检测视为两类分类问题，并使用带有径向基函数核的支持向量机来解决此问题。作者评估了三个基准数据集上的实验。实验结果表明，所提方法的准确性优于大多数最新方法。