Morph Deterction from Single Face Image: a Multi-Algorithm Fusion Approach
Morph Deterction from Single Face Image: a Multi-Algorithm Fusion Approach
复制标题
单人脸图像的变形检测：一种多算法融合方法
DOI：
10.1145/3230820.3230822
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
C. Busch
中科院分区：
文献类型：
--
作者：
U. Scherhag;C. Rathgeb;C. Busch
人脸、指纹和虹膜识别系统对基于变形生物特征样本的攻击的脆弱性在最近已经建立。然而，到目前为止，变形生物特征样本的可靠检测仍然是一个未解决的研究挑战。在这项工作中，我们提出了第一个多算法融合的方法来检测变形的人脸图像。FRGCv 2人脸数据库用于创建一组4，808个变形和2，210个真实人脸图像，这些图像被分为训练集和测试集。使用四种类型的互补特征提取算法从单个裁剪的面部图像中提取特征，包括纹理描述符，关键点提取器，梯度估计器和基于深度学习的方法。通过执行由四种不同类型的特征提取器获得的比较分数的分数级融合，实现了2.8%的检测等错误率（D-EER）。与实现5.5%的D-EER的最佳单算法方法相比，所提出的多算法融合系统的D-EER几乎低两倍，证实了所提出的方法的可靠性。
The vulnerability of face, fingerprint and iris recognition systems to attacks based on morphed biometric samples has been established in the recent past. However, so far a reliable detection of morphed biometric samples has remained an unsolved research challenge. In this work, we propose the first multi-algorithm fusion approach to detect morphed facial images. The FRGCv2 face database is used to create a set of 4,808 morphed and 2,210 bona fide face images which are divided into a training and test set. From a single cropped facial image features are extracted using four types of complementary feature extraction algorithms, including texture descriptors, keypoint extractors, gradient estimators and a deep learning-based method. By performing a score-level fusion of comparison scores obtained by four different types of feature extractors, a detection equal error rate (D-EER) of 2.8% is achieved. Compared to the best single algorithm approach achieving a D-EER of 5.5%, the D-EER of the proposed multi-algorithm fusion system is al- most twice as low, confirming the soundness of the presented approach.