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Short-Term User Authentication Using Eyebrows Biometric For Smartphone Devices

机译:智能手机设备使用眉毛生物特征识别的短期用户身份验证

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With the unprecedented mobile technology revolution, biometrics have become a viable alternative to PINs and passwords for secured mobile access and transactions. Recent research has shown the potential of eyebrows as an independent biometric modality for recognition. Eyebrows leverage the benefit of being one-sixth of the facial region, and is therefore computationally efficient and offer fast throughput. The aim of this paper is to exploit eyebrow region for mobile user authentication, and to show the impact factor such as eyeglasses existence on eyebrow-based user authentication. To this aim, Histogram of Oriented Gradients (HOG), a local descriptor, and GIST, a global descriptor, extracted from left and right eyebrow regions are evaluated. The reported results on a publicly available VISOB dataset collected through mobile devices. show a minimum Equal Error Rate (EER) of 3.23% and Area under curve (AUC) of 0.9916 obtained from fusion of left and right eyebrow regions at score level using GIST descriptor. Further, comparative evaluation with existing methods is performed by evaluating the proposed method on FERET face dataset obtaining minimum Equal Error Rate (EER) of 1.08% and Area under curve (AUC) of 0.9990 on fusion of left and right eyebrow regions at score level without eyeglasses using HOG descriptor.
机译:随着史无前例的移动技术革命,生物识别技术已成为PIN和密码的可行替代方案,以确保安全的移动访问和交易。最近的研究表明,眉毛作为一种独立的生物特征识别方式具有潜力。眉毛利用了占面部区域六分之一的优势,因此计算效率高,并提供快速的吞吐能力。本文的目的是利用眉毛区域进行移动用户身份验证,并展示诸如眼镜的存在等因素对基于眉毛的用户身份验证的影响。为此,评估了从左右眉毛区域中提取的局部梯度直方图(HOG)和局部描述符GIST。通过移动设备收集的可公开获得的VISOB数据集上的报告结果。显示使用GIST描述符从得分水平左右眉毛区域融合获得的最小均等错误率(EER)为3.23%和曲线下面积(AUC)为0.9916。此外,通过在FERET人脸数据集上评估所提出的方法来进行与现有方法的比较评估,从而在得分级别的左右眉毛区域融合中获得最小均等错误率(EER)为1.08 \%和曲线下面积(AUC)为0.9990没有使用HOG描述符的眼镜。

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