A Proposal for Deep Online Facial Verification using Selfies and Id document

Currently, cybersecurity has become one of the most important issues in the society, due to the growing technological evolution and use of digital platforms by organizations to connect with their users. A non-invasive biometric way of accessing an organizational platform is by verifying the presence of the user's face given by selfie photography in a database of authorized users. However, this procedure requires the prior construction of a database of authorized users, which prevents online verification of a person's identity. A feasible way to carry out an online verification is by comparing the photos of the identity document and the person's selfie. A system that allows this verification will improve the security of online use of a platform that prevents fraud and identity theft. In this work, we propose a deep neural network that allows verifying the identity of a person considering as inputs the photographs of an identity document and a selfie. This document presents experiments with various neural networks considering a public database of real camera photographs and identity documents. The results show that the use of a deep neural network with an ArcFace loss function configured with the database images of ID photos achieved a recognition rate of over 94%. Additionally, we test this procedure in a small validation sample of Chilean people obtaining similar rate. As future work, we propose the use of larger databases based on Chilean document data.