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

Cross-Domain Deep Face Matching for Real Banking Security Systems