DocFace: Matching ID Document Photos to Selfies
This addresses the need for efficient and accurate identity verification in daily transactions and services, though it is an incremental improvement over applying existing face recognition methods to this specific domain.
The paper tackles the problem of automatically matching ID document photos to live selfies for identity verification, achieving a significant improvement in True Acceptance Rate (TAR) from 61.14% to 92.77% at a False Acceptance Rate (FAR) of 0.1% using a domain-specific network trained with transfer learning.
Numerous activities in our daily life, including transactions, access to services and transportation, require us to verify who we are by showing our ID documents containing face images, e.g. passports and driver licenses. An automatic system for matching ID document photos to live face images in real time with high accuracy would speedup the verification process and remove the burden on human operators. In this paper, by employing the transfer learning technique, we propose a new method, DocFace, to train a domain-specific network for ID document photo matching without a large dataset. Compared with the baseline of applying existing methods for general face recognition to this problem, our method achieves considerable improvement. A cross validation on an ID-Selfie dataset shows that DocFace improves the TAR from 61.14% to 92.77% at FAR=0.1%. Experimental results also indicate that given more training data, a viable system for automatic ID document photo matching can be developed and deployed.