Ensuring the security of transactions is currently one of the major challenges facing banking systems. The usage of face for biometric authentication of users is becoming adopted worldwide due its convenience and acceptability by people, and also given that, nowadays, almost all computers and mobile devices have built-in cameras. Such user authentication approach is attracting large investments from banking and financial institutions, especially in cross-domain scenarios, in which facial images from ID documents are compared with digital self-portraits (selfies) taken with the cameras of mobile devices, for the automated opening of new checking accounts or financial transactions authorization. In this work, besides of collecting a large cross-domain face database, with 27,002 real facial images of selfies and ID documents (13,501 subjects) captured from the systems of the major public Brazilian bank, we propose a novel approach for such cross-domain face matching based on deep features extracted by two well-referenced Convolutional Neural Networks (CNN). Results obtained on the large dataset collected, which we called FaceBank, with accuracy rates higher than 93%, demonstrate the robustness of the proposed approach to the cross-domain problem (comparing faces in IDs and selfies) and its feasible application in real banking security systems.

The networks fight each other and improve together, like two humans playing against each other in a game.

We've all heard about racial bias in artificial intelligence via the media, whether it's found in recidivism software or object detection that mislabels African American people as Gorillas. Due to the increase in the media attention, people have grown more aware that implicit bias occurring in people can affect the AI systems we build. Early this week, I was honored to give a talk on Racial Bias in Facial Recognition at PyCascades, a new regional Python conference. Last week I wrote a blog post on learning facial recognition through OpenFace where I went into deeper detail about both facial recognition and the OpenFace architecture, so if you want to give that a read through before checking out this talk, I highly encourage it. Using OpenFace as an example face recognition model, this talk will cover the basics of facial recognition and why it's important to have diverse datasets when building out a model.

I just wanted to build something cool using machine learning on a bunch of public images. But after showing it to a couple of my "friends" they thought it was too creepy and Instagram might sue me for breaking their platform policy and I should stop doing it. So, I did what most sane people would do - write a blog post detailing how I did it, and open source it. Whats the worst that could happen? Err.. that's a good question.

The networks fight each other and improve together, like two humans playing against each other in a game.

OpenFace is a Python and Torch implementation of face recognition with deep neural networks and is based on the CVPR 2015 paper FaceNet: A Unified Embedding for Face Recognition and Clustering by Florian Schroff, Dmitry Kalenichenko, and James Philbin at Google. Torch allows the network to be executed on a CPU or with CUDA. This research was supported by the National Science Foundation (NSF) under grant number CNS-1518865. Additional support was provided by the Intel Corporation, Google, Vodafone, NVIDIA, and the Conklin Kistler family fund. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and should not be attributed to their employers or funding sources.