One of the most groundbreaking features of the iPhone X is its facial recognition system, Face ID. With the introduction of a borderless design, Apple had to develop a new, secure, and convenient way to unlock the device. Unlike other manufacturers who still rely on fingerprint sensors in various positions, Apple took a bold step by introducing a revolutionary method: using facial recognition. Let’s dive into how this works.
Face ID uses an advanced depth camera to create a 3D map of the user's face. In addition, an infrared camera captures the user's face, making it more reliable under different lighting conditions. Through deep learning, the phone can learn the user's facial features in great detail, allowing it to recognize them instantly whenever they pick up the device. What's even more impressive is that Apple claims Face ID is safer than Touch ID, with an error rate as low as one in a million.
I was fascinated by how Apple implemented Face ID and wanted to understand how deep learning could be used to replicate this process and optimize each step. In this article, I will demonstrate how to build a Face ID-like algorithm using Keras. I’ll explain the architectural choices I made and use a Kinect sensor to showcase some experimental results. The Kinect provides RGB-D data similar to what the iPhone X’s front camera captures, albeit with a larger device. Let’s start exploring Apple’s innovation.
Understanding Face ID
“The neural network supporting Face ID isn’t just doing classification.â€
The setup process for Face ID is quite intuitive. Users simply look at the phone and slowly move their head to capture different angles of their face. This allows the system to create a comprehensive model of the user's face. This quick registration process gives us insight into the power of the underlying learning algorithm. For instance, the neural network behind Face ID goes beyond simple classification.
Apple’s approach to training Face ID involves pre-training a complex model in their labs before deploying it on the user’s device. This suggests that Face ID might be powered by a Siamese convolutional neural network, which maps faces into a low-dimensional space using contrastive loss. This enables the system to perform “one-shot learning,†where it can recognize a face after seeing just a few examples.
From digital recognition to face recognition, Siamese networks are designed to calculate the similarity between two inputs. By mapping data into a feature space, these networks can distinguish between different identities while keeping similar ones close. Imagine encoding a dog’s characteristics into a vector—similar dogs would have similar vectors. A Siamese network can do this automatically, much like an encoder.
Using this technique, a large dataset of faces can be used to train the model to identify similarities. If you have the resources (like Apple), you can introduce harder examples to make the network robust against attacks, such as masks or twins. One major advantage is that the model is ready to use without further training. During setup, it only needs a few photos of the user’s face to create a reference point in the embedding space. It can also adapt to changes in appearance, such as wearing glasses or growing a beard, by updating the reference vectors accordingly.
Now, let’s see how to implement this using Keras.
Implementing Face ID in Keras
To begin, we need a dataset. I found an RGB-D face dataset online that includes images taken from various angles and expressions. This dataset closely resembles the kind of data used by Face ID.
You can find the implementation here: [https://github.com/normandipalo/faceID_beta](https://github.com/normandipalo/faceID_beta)
And the Colab notebook here: [https://colab.research.google.com/drive/1OynWNoWF6POTcRGFG4V7KW_EGIkUmLYI](https://colab.research.google.com/drive/1OynWNoWF6POTcRGFG4V7KW_EGIkUmLYI)
I built a convolutional network based on SqueezeNet, which takes an RGB-D face image (four channels) as input and outputs the distance between two embeddings. During training, the network minimizes the distance between images of the same person and maximizes the distance between images of different people.
After training, the network can map faces into a 128-dimensional space, grouping similar faces together and separating them from others. To unlock the device, the system calculates the distance between the current face and the stored reference. If it’s below a threshold, the device unlocks.
I used t-SNE to visualize the 128-dimensional space in 2D. Each color represents a different person, and as you can see, the model has learned to group the faces correctly. Interestingly, when using PCA for dimensionality reduction, the clustering pattern remains consistent.
Testing the Model
Now, let’s simulate the Face ID process. First, we register the user by collecting multiple images from the dataset. The system computes the embedding for each pose and stores it locally.
Then, during the unlock phase, if the same user tries to unlock the device, the distance between the current face and the registered one is small—around 0.30. However, for different users, the average distance is about 1.1. Setting a threshold around 0.4 should be sufficient to prevent unauthorized access.
This experiment demonstrates how a deep learning-based system can mimic the functionality of Face ID, offering both security and convenience.
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