论文标题
脸部检测的合奏模型
An Ensemble Model for Face Liveness Detection
论文作者
论文摘要
在本文中，我们提出了一种被动方法，用于检测面部表现攻击，也可以使用合奏深度学习技术检测到面部livestion检测。 Face Livess检测是在线入职/交易过程中用户身份验证涉及的关键步骤之一。在身份验证期间，未经身份验证的用户试图通过几种方式绕过验证系统，例如，他们可以从社交媒体中捕获用户照片，并使用用户面部打印件或使用移动设备的数字照片进行冒名顶替的攻击，甚至创建更复杂的攻击，例如视频重播攻击。我们试图理解不同的攻击方法，并创建了一个内部大规模数据集，涵盖了所有攻击以训练强大的深度学习模型。我们提出了一种合奏方法，其中学会了面部和背景区域的多个特征来预测用户是真正的还是攻击者。
In this paper, we present a passive method to detect face presentation attack a.k.a face liveness detection using an ensemble deep learning technique. Face liveness detection is one of the key steps involved in user identity verification of customers during the online onboarding/transaction processes. During identity verification, an unauthenticated user tries to bypass the verification system by several means, for example, they can capture a user photo from social media and do an imposter attack using printouts of users faces or using a digital photo from a mobile device and even create a more sophisticated attack like video replay attack. We have tried to understand the different methods of attack and created an in-house large-scale dataset covering all the kinds of attacks to train a robust deep learning model. We propose an ensemble method where multiple features of the face and background regions are learned to predict whether the user is a bonafide or an attacker.
