DocFace +：ID证件照与自拍照匹配
DocFace+: ID Document to Selfie Matching
摘要：
我们日常生活中的许多活动都需要我们通过向人类操作员出示包含人脸图像的身份证件（如护照和驾驶执照）来验证我们是谁。然而，这个过程是缓慢的，劳动密集型和不可靠的。因此，需要一种用于实时的且高精度地将ID文档照片与活体面部图像（自拍）匹配的自动化系统。在本文中，我们提出DocFace+来满足这一目标。我们首先展示了基于梯度的优化方法收敛缓慢（由于分类器权重的拟合不足），当许多类的样本非常少时，这是现有ID自拍数据集的一个特征。为了克服这个缺点，我们提出了一种方法，称为动态权重印记（DWI），更新分类器的权重，它允许更快的收敛和更概括的表示。接下来，训练一对具有部分共享参数的兄弟网络，以学习具有特定领域参数的统一人脸表示。在ID自拍数据集上的交叉验证表明，虽然公开可用的通用人脸匹配器（SphereFace）在0.1%的错误接受率（FAR）下只能实现59.29+-1.55%的真实接受率（TAR），但DocFace+将TAR提高到97.51+-0.40%。
英文摘要：
Numerous activities in our daily life require us to verify who we are by showing our ID documents containing face images, such as passports and driver licenses, to human operators. However, this process is slow, labor intensive and unreliable. As such, an automated system for matching ID document photos to live face images (selfies) in real time and with high accuracy is required. In this paper, we propose DocFace+ to meet this objective. We first show that gradient-based optimization methods converge slowly (due to the underfitting of classifier weights) when many classes have very few samples, a characteristic of existing ID-selfie datasets. To overcome this shortcoming, we propose a method, called dynamic weight imprinting (DWI), to update the classifier weights, which allows faster convergence and more generalizable representations. Next, a pair of sibling networks with partially shared parameters are trained to learn a unified face representation with domain-specific parameters. Cross-validation on an ID-selfie dataset shows that while a publicly available general face matcher (SphereFace) only achieves a True Accept Rate (TAR) of 59.29+-1.55% at a False Accept Rate (FAR) of 0.1% on the problem, DocFace+ improves the TAR to 97.51+-0.40%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
