Hello Norman. Recently you’ve presented to the members of the Facebook Developer Circle community your talk on “How I implemented iPhone X’s Face ID using Deep Learning”. Would you like to tell us what it was about?

I gladly accepted the invitation to give a speech at the Facebook Developer Circle community. I always watch Apple keynote speeches, in which they present new products and as a fan, I’m very interested. When I saw the iPhone X, I was very surprised, especially by the deep learning aspect used to unlock the device by face and no longer by fingerprint. The amazing thing is that it needs to “see” the phone owner’s face for a few seconds and then can remember him or her forever with various different nuances. As such, it’s a challenge that aroused my curiosity, and I started to do some scientific research to understand how to implement it. I found similar datasets that combined photos of people’s faces with photos that gave depth, so I created a neural network called Siamese, which has the advantage of being trained offline and can learn a new face quickly, compared to the classic deep learning that takes a long time to learn something new.

Later on, I wrote an article on my blog because I like to share these things with the others. After that, I woke up in the morning with hundreds of views! Apparently, someone had posted the article on Hacker News, which is a very famous news portal in the tech industry, and it was translated into five languages.

Siamese neural network, what is it? Can you explain it to us?

A Siamese neural network is composed of two identical neural networks. Imagine the same network taking two images as input and instead of saying they are of dogs, cats or a picture of Norman or Simon, simply learning to tell what is the distance between the two photos, or how much they resemble each other. What makes this difference from classifying dog and cat, seeing the difference? I can take millions of photos of people offline, we get a neural network, we perfect it over time, we take two faces of people and can tell if they are the same person or not. In conclusion, this network tells you how different or similar the images are to one another.

That’s super interesting. We’re really looking forward to other talks like this. But let’s change the topic for now. For those that did not have a chance to meet you in person, can you tell us a bit about yourself?

My name is Norman Di Palo, I’m from Naples but I live in Rome, though currently I live in Helsinki and I work as an AI researcher in a startup. I have a degree in Automation Engineering where I worked on control and intelligent systems. That’s how I started being interested in cognitive robotics, so I started looking for a Master’s degree that would also include Artificial Intelligence. I’m about to graduate now and I’m working on my Master’s thesis. As for as my work, I founded a startup together with my colleagues. I’m also working in a Pi School and we are involved in making a Pi school where, twice a year, we choose 15 engineering students, any talented figures from around the world. We carry out AI studies and solve problems given to us by companies, including Amazon, BNL, Cisco and Poste Italiane. I am an AI Advisor and Project Manager of some of these projects.