How Facial Recognition Works with Face Masks

How it started

Face recognition existed in one form or another since the 1960s. Recent technological advancements brought it to our daily lives:

• In 2014, Facebook launches the DeepFace program. DeepFace’s accuracy rate was 97,25%, just 0.28% below that of a human.
• In 2015, Google went one better with FaceNet. On the widely used Labeled Faces in the Wild (LFW) dataset, FaceNet achieved a new record accuracy of 99.63%
• In 2017 Apple launches iPhone X with Face ID.
• In 2019, COVID-19 helped face recognition technology gain even more momentum.
• In 2021 NIST technology started testing masked face recognition software and achieved success.

With the release of MegaFace researchers started to use new benchmarks. MegaFace metric tests models based on their ability to recognize faces in the presence of many “distractors”. “Distractors” exist when there are many potential faces to choose from.

Current Challenge

With the spread of COVID-19 wearing face masks became obligatory. At least for most of the population.

This created a problem for the current identification systems. For example, Apple’s FaceID struggled to recognize faces with masks.

https://faceidmasks.com offered one solution. They created face masks that mimic our facial features. But does this solve the problem on a global scale? Today, many companies are looking to hire data scientists to overcome the problem of facial recognition with medical masks.

Continue reading to find out how we tackled this challenge!

New Solution

We created a system that can recognize faces with medical masks. And now you can have the tools to build your own solution as the code is available for the public.

How do we do that? We created such data augmentations that transform the first training dataset into that of the face with a medical mask.

What does Data Augmentation do?

There are several Data Augmentation techniques that include cropping, padding, and horizontal flipping. They are used to train large neural networks. It looks like this:

• It increases the amount of training data using information available from this data, so it can capture as much data variation as possible.
• It creates more data to get better generalization in your model

Three-step solution:

But in our case, it will solve the warping texture task in 3 simple steps:

• Find facial landmarks
• Process Triangulation
• Complete Matching

Find facial landmarks

We will use face-alignment python library in order to extract keypoints of the initial face.

Triangulation process

From the handcrafted dataset with the medical masks by cropping and triangulation process we managed to extract ~250 masks, which will be matched to other persons. The facial masks database can be found in the solution repository via this link.

Medical mask matching

The last piece of our augmentation is to extract keypoints, triangulate the ones that we need and then match the randomly extracted mask from the previous step to our destination face.

Another advantage of this solution is that it can recognize faces in various positions, including rotation of the face. The database of medical masks is stored in JSON. It includes calculated parameters of rotation. This allows us to match images with face rotation only with those masks that are falling in concrete intervals of rotation for a given face.

You can replicate the whole process with this colab notebook and even prepare your own dataset with a provided pipeline.

Pipeline for face recognition

Deployment of neural network solutions in Data Science starts from, guess what?

Data

In order to bring this solution to reality, we will manage to train ArcFace model on VGGFace2 dataset:

We already preprocessed the part of this dataset with medical masks and it's available for downloading from this Google Drive link.

ArcFace

From the very beginning of this article, we mentioned MegaFace dataset which now is the indicator for face recognition solutions. From its leaderboard we will pick one of the best so far - ArcFace: Additive Angular Margin Loss for Deep Face Recognition

One of the main challenges in feature learning using Deep Convolutional Neural Networks (DCNNs) for largescale face recognition is the design of appropriate loss functions that enhance discriminative power. This is how the authors of ArcFace paper address this problem:

In this paper, we propose an Additive Angular Margin Loss (ArcFace) to obtain highly discriminative features for face recognition. The proposed ArcFace has a clear geometric interpretation due to the exact correspondence to the geodesic distance on the hypersphere. We present arguably the most extensive experimental evaluation of all the recent state-of-the-art face recognition methods on over 10 face recognition benchmarks including a new large-scale image database with trillion levels of pairs and a large-scale video dataset. We show that ArcFace consistently outperforms the state-of-the-art and can be easily implemented with negligible computational overhead

This is a process of training a DCNN for face recognition supervised by the ArcFace loss:

If you want to dive deeper with the theoretical part of this solution, we recommend checking out the original research and this medium article.

Implementation

The whole pipeline code with a detailed description provided in google colab notebook. We will use the datasets and medical masks we mention above in the article and you are welcome to use it too by default, or get along with your own dataset - our pipeline is 100% scalable and user-friendly, so don't forget to check the evaluated results.

Results

We achieved 58 percents accuracy with our pipeline on the custom test dataset. And it will be higher on LFW metric. The ability to show impressive results for such limited training time proves that the pipeline is able to solve face recognition with medical masks tasks.