In recent years, fingerprint authentication systems have been extensively deployed in various applications, including attendance systems, authentications on smartphones, mobile payment authorizations, as well as various safety certifications. However, similar to the other biometric identification technologies, fingerprint recognition is vulnerable to artificial replicas made from cheap materials, such as silicon, gelatin, etc. Thus, it is especially necessary to distinguish whether a given fingerprint is a live or a spoof one prior to such authentication. In order to solve the problems above, a novel local descriptor named Weber local binary descriptor for fingerprint liveness detection (FLD) has been proposed in this paper. The method consists of two components: the local binary differential excitation component that extracts intensity-variance features and the local binary gradient orientation component that extracts orientation features. The co-occurrence probability of the two components is calculated to construct a discriminative feature vector, which is fed into support vector machine (SVM) classifiers. The effectiveness of the proposed method is intuitively analyzed on the image samples and numerically demonstrated by Mahalanobis distance. Experiments are performed on two public databases from FLD competitions from 2011 and 2013. The results have proved that the proposed method obtains the best detection accuracy among the existing image local descriptors in FLD.

中文翻译：

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一种用于指纹活体检测的新型韦伯本地二进制描述符

近年来，指纹认证系统已广泛应用于各种应用，包括考勤系统、智能手机认证、移动支付授权以及各种安全认证。然而，与其他生物特征识别技术类似，指纹识别容易受到由廉价材料制成的人工复制品的影响，例如硅、明胶等。因此，特别需要事先区分给定的指纹是活的还是伪造的。到这样的认证。为了解决上述问题，本文提出了一种新的局部描述符，称为 Weber 局部二进制描述符，用于指纹活体检测（FLD）。该方法由两部分组成：提取强度方差特征的局部二元差分激励分量和提取方向特征的局部二元梯度方向分量。计算两个分量的共现概率以构建判别特征向量，将其输入支持向量机 (SVM) 分类器。在图像样本上直观地分析了所提出方法的有效性，并通过马氏距离进行了数值论证。在 2011 年和 2013 年 FLD 比赛的两个公共数据库上进行了实验。结果证明，所提出的方法在 FLD 中现有的图像局部描述符中获得了最好的检测精度。计算两个分量的共现概率以构建判别特征向量，将其输入支持向量机 (SVM) 分类器。在图像样本上直观地分析了所提出方法的有效性，并通过马氏距离进行了数值论证。在 2011 年和 2013 年 FLD 比赛的两个公共数据库上进行了实验。结果证明，所提出的方法在 FLD 中现有的图像局部描述符中获得了最好的检测精度。计算两个分量的共现概率以构建判别特征向量，将其输入支持向量机 (SVM) 分类器。在图像样本上直观地分析了所提出方法的有效性，并通过马氏距离进行了数值论证。在 2011 年和 2013 年 FLD 比赛的两个公共数据库上进行了实验。结果证明，所提出的方法在 FLD 中现有的图像局部描述符中获得了最好的检测精度。