Detection of Age-Induced Makeup Attacks on Face Recognition Systems Using Multi-Layer Deep Features
Detection of Age-Induced Makeup Attacks on Face Recognition Systems Using Multi-Layer Deep Features
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DOI：
10.1109/tbiom.2019.2946175
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发表时间：
2020-01
期刊：
影响因子：
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通讯作者：
Ketan Kotwal;Z. Mostaani;S. Marcel
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文献类型：
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作者：
Ketan Kotwal;Z. Mostaani;S. Marcel
化妆是一种简单易用的工具，可以改变人脸的外观，从而对人脸识别（FR）系统造成演示攻击。这些攻击，尤其是模仿衰老的攻击，由于与真实（非化妆）外观非常相似，因此很难被发现。化妆还会降低识别系统和使用人脸作为输入的各种算法的性能。面部化妆检测是减少这些问题的有效禁止措施。这项工作提出了一种基于深度学习的演示攻击检测（PAD）方法来识别面部化妆。我们建议使用卷积神经网络（CNN）来提取特征，这些特征可以区分带有年龄引起的面部化妆（攻击）和不化妆（真实）的演示。这些基于形状和纹理线索的特征描述符是由 CNN 的多个中间层构建的。我们引入了一个新的数据集 AIM（年龄诱导化妆），每个数据集包含 200 多个老年化妆和真实化妆的视频演示。我们的实验表明，AIM 中的化妆导致最近基于 CNN 的 FR 系统的中值匹配分数下降了 14%。我们证明了所提出的 PAD 方法的准确性，其中 AIM 数据集中 93% 的呈现被正确分类。在额外的测试中，它也优于现有的通用化妆品检测方法。对基于形状和纹理的特征的分类分数进行简单的分数级融合，可以进一步提高所提出的化妆检测器的准确性。
Makeup is a simple and easy instrument that can alter the appearance of a person’s face, and hence, create a presentation attack on face recognition (FR) systems. These attacks, especially the ones mimicking ageing, are difficult to detect due to their close resemblance with genuine (non-makeup) appearances. Makeups can also degrade the performance of recognition systems and of various algorithms that use human face as an input. The detection of facial makeups is an effective prohibitory measure to minimize these problems. This work proposes a deep learning-based presentation attack detection (PAD) method to identify facial makeups. We propose the use of a convolutional neural network (CNN) to extract features that can distinguish between presentations with age-induced facial makeups (attacks), and those without makeup (bona-fide). These feature descriptors, based on shape and texture cues, are constructed from multiple intermediate layers of a CNN. We introduce a new dataset AIM (Age Induced Makeups) consisting of 200+ video presentations of old-age makeups and bona-fide, each. Our experiments indicate makeups in AIM result in 14% decrease in the median matching scores of a recent CNN-based FR system. We demonstrate accuracy of the proposed PAD method where 93% presentations in the AIM dataset are correctly classified. In additional testing, it also outperforms existing methods of detection of generic makeups. A simple score-level fusion, performed on the classification scores of shape- and texture-based features, can further improve the accuracy of the proposed makeup detector.