Cross-Domain Deep Face Matching for Real Banking Security Systems

TL;DR

This paper introduces a new deep learning architecture for cross-domain face matching, specifically comparing selfies with ID photos, to enhance security in banking systems, achieving over 93% accuracy on a large real-world dataset.

Contribution

The study presents a novel deep neural network architecture and a large cross-domain face dataset for improved face matching in banking security applications.

Findings

Achieved over 93% accuracy on FaceBank dataset

Demonstrated robustness of the proposed method in real banking scenarios

Collected a large dataset of 27,002 images from Brazilian bank databases

Abstract

Ensuring the security of transactions is currently one of the major challenges that banking systems deal with. The usage of face for biometric authentication of users is attracting large investments from banks worldwide due to its convenience and acceptability by people, especially in cross-domain scenarios, in which facial images from ID documents are compared with digital self-portraits (selfies) for the automated opening of new checking accounts, e.g, or financial transactions authorization. Actually, the comparison of selfies and IDs has also been applied in another wide variety of tasks nowadays, such as automated immigration control. The major difficulty in such process consists in attenuating the differences between the facial images compared given their different domains. In this work, in addition to collecting a large cross-domain face dataset, with 27,002 real facial images of selfies and ID documents (13,501 subjects) captured from the databases of the major public Brazilian bank, we propose a novel architecture for such cross-domain matching problem based on deep features extracted by two well-referenced Convolutional Neural Networks (CNN). Results obtained on the dataset collected, called FaceBank, with accuracy rates higher than 93%, demonstrate the robustness of the proposed approach to the cross-domain face matching problem and its feasible application in real banking security systems.