Detecting morphed face images
Detecting morphed face images
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DOI：
10.1109/btas.2016.7791169
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发表时间：
2016-09
期刊：
影响因子：
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通讯作者：
Ramachandra Raghavendra;K. Raja;C. Busch
中科院分区：
文献类型：
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作者：
Ramachandra Raghavendra;K. Raja;C. Busch
沿着用于人员验证的电子机读旅行证件（eMRTD）一起广泛部署的自动边境控制（ABC）使得面部生物识别技术在边境控制应用中成为一个突出的用例。许多国家根据申请人提交的打印生物特征面部照片签发eMRTD护照。一些国家提供护照更新的门户网站，公民可以上传他们的脸部照片。这些应用程序允许照片被修改以美化数据主体的外观或被变形以隐藏申请人身份的可能性。具体而言，如果eMRTD护照签发时带有变形的面部图像，则两个或多个数据主体，可能是已知的申请人和一个或多个未知的同伴，可以使用这种护照通过边境管制。在这项工作中，我们提出了一种新的方案来检测变形的人脸图像的基础上提取的面部微纹理使用统计独立的过滤器，训练自然图像。该方法利用二值化统计图像特征（BSIF）得到人脸图像的微观纹理变化，并利用线性支持向量机（SVM）进行决策。这是第一个工作进行的变形人脸图像检测。大量的实验进行了大规模的数据库中的450变形的人脸图像创建使用110个独特的主题具有不同的种族，年龄和性别，表明上级性能。
Widespread deployment of Automatic Border Control (ABC) along with the electronic Machine Readable Travel Documents (eMRTD) for person verification has enabled a prominent use case of face biometrics in border control applications. Many countries issue eMRTD passports on the basis of a printed biometric face photo submitted by the applicant. Some countries offer web-portals for passport renewal, where citizens can upload their face photo. These applications allow the possibility of the photo being altered to beautify the appearance of the data subject or being morphed to conceal the applicant identity. Specifically, if an eMRTD passport is issued with a morphed facial image, two or more data subjects, likely the known applicant and one or more unknown companion(s), can use such passport to pass a border control. In this work we propose a novel scheme to detect morphed face images based on facial micro-textures extracted using statistically independent filters that are trained on natural images. Given a face image, the proposed method will obtain a micro-texture variation using Binarized Statistical Image Features (BSIF), and the decision is made using a linear Support Vector Machine (SVM). This is first work carried out towards detecting the morphed face images. Extensive experiments are carried out on a large-scale database of 450 morphed face images created using 110 unique subjects with different ethnicity, age, and gender that indicates the superior performance.