IEEE Project Abstract

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 photographs to live face images (selfies 1 ) 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, 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 (InsightFace) only achieves a true accept rate (TAR) of 88.78 ± 1.30% at a false accept rate of 0.01% on the problem, DocFace+ improves the TAR to 95.95 ± 0.54%. 1 Technically, the word “selfies” refers to self-captured photos from mobile phones. But here, we define “selfies” as any self-captured live face photos, including those from mobile phones and kiosks.