Face recognition is a computer vision application that has played an active role in our lives in one way or another. From something as mundane as recognizing and suggesting which friend to tag on a Facebook image to unlocking our smartphones or other devices at the speed of light — face recognition algorithms make it happen and more. As people, we’re equipped with the skills needed to recognize hundreds of faces and recall who they belong to. Our current developments in technology are what allow us to give digital systems the ability to mimic human processes as accurately as possible. As a data scientist, however, it’s necessary to take a much closer look beyond the surface level if you want to take on the task of face recognition in practice.
In this article, we’ll cover all the ins and outs of facial recognition, including:
- What is face recognition?
- Face detection vs. face recognition
- How face recognition works
- Who has access to biometrics
- Use cases and applications
- Key takeaways
What is face recognition?
Before we dive into the inner workings of face recognition software, it’s important to determine what is AI face recognition exactly. Luckily, the term is straightforward and doesn’t leave much open to interpretation — face recognition is the task of detecting a face and identifying a person’s identity from an input visual in a digital system. From the result, we understand whether or not the identity of the person precisely matches anything from an existing database. Modern-day face recognition algorithms give us the capability to carry out that process on an array of visuals — from images to pre-recorded videos and even real-time footage.
The data used during face recognition is referred to as biometric information — similar to that of when software stores information about your fingerprint, iris/retina, or voice. Detecting the presence of a face is relatively simple, but it is the biometric data that is necessary to differentiate and label the billions of faces that exist.
Face detection vs. face recognition
It’s easy to misconceive face detection as face recognition when initially being introduced to the subject. To know the difference between face detection and face recognition, you need a basic understanding of object detection. With object detection, the primary aim of the task is to note the location of a particular object in a visual, mark its limits with a bounding box, and attach a category to that object. If we merely want to carry out face detection, that same process is applied to detecting faces in a given image or video footage to simply locate the position and boundaries of a face. It will not, however, give us more detailed information, such as if the face belongs to an existing database or whose face exactly is detected.
You may wonder in which scenarios face detection alone can be resourceful without going a step further to carry out face recognition. As a matter of fact, there are several such circumstances, such as when you need to take a headcount of the people present in a crowd, know whether or not there is a face present, and so on.
How face recognition works
Now we can determine how to do facial recognition. It’s typically executed through Python since this approach is arguably the quickest. The process of face recognition using machine learning methods follows these five main steps:
Detecting a face
Remember how we just talked about face detection? Well, it’s a vital first step in the facial recognition process. The machine first needs to detect the face(s) in an image to first establish if there are any faces at all, which will be the source material for the later steps.
Once a face has been established and detected, the next step is to determine the alignment of the face. One of the drawbacks of current face recognition technology is that the face needs to be clearly shown (free from occlusion, not covered by clothing or other objects) and looking in the direction of the camera to provide a higher probability of accuracy. You can train a machine learning model to detect the key points of a face (chin, eyes, mouth, etc.) and slightly tilt the image to center it in order to get a front-facing alignment.
Face measurement & extraction
Next, once a face is detected and the primary characteristics of the face are evident from the image, the necessary features for face recognition can be extracted. That includes but is not limited to the measurements of the eyes, nose, mouth, and so on. This step will go on to help find similar matches of the extracted features from the database.
Only then can we execute the actual process of facial recognition. That is when a final algorithm will compare the measurements extracted from the features to the database to search for potential matches.
Finally, the facial features can be compared between visuals until a complete match is verified. If an exact match is not found in the database, then the face will remain unverified. Just like any ML system, you must keep in mind that the functionality of the algorithm relies on both the quantity and quality of the training data. You can train the model either with your own data or with the help of open-source datasets.
Who has access to biometrics
Biometrics is an important consideration for the process of facial recognition. As we established, biometrics are any or all biological measurements of a person. Face recognition requires vision-based biometrics as in the type of measurements that are visually present to the eye as opposed to other non-vision biometrics like odor, handwriting, height, weight, and so on. The most common vision-based biometrics are fingerprints and iris/retina information, but are you allowed to store this data when setting up your ML model? You need to acquire this information from the individual with their permission in order to use it, which is why modern-day biometric storing and data selling among tech giants is a controversial debate. Typically, the biometric data is stored on the user’s end device to ensure their private information is maintained and not tampered with.
Use cases and applications
As we’ve established, facial recognition software exists all around us, and the applications are essentially uncountable. With that said, let’s take a look at a few of the most prominent facial recognition applications that are paving the path to a safer and more efficient future.
Device & access security
If we only imagine exciting scenes like a person unlocking a secret lair with a face scanner in old spy movies, now, technology like that is accessible in the palms of everyone’s hands. From smartphones to tablets and smart vehicles, face recognition software allows us to limit who has access to our devices to maintain privacy. Whole vehicles and rooms can also require facial authorization to only allow owners access to private property.
Shoplifting & crime
People who manage to shoplift once won’t have much luck a second time around if the store is equipped with facial recognition security measures to compare the flow of shoppers to shoplifters from their database. In many cases, if a direct match is detected, it can trigger nearby police or security to detain the criminal on the spot. This can be expanded to all types of stores and institutions, including the help of local police to handle criminal activity in the area efficiently.
If you’ve crossed an international border before, then it should come as no surprise that a face scan is done during border control to track immigration and prohibit the entry of criminals. The faces are scanned and run through a database before a person is “cleared” to cross the border. This makes for easier, quicker, and more effective tracking of national security.
Hospitals & assisted living
Tech-forward medical institutions and assisted living centers are rolling out cameras equipped with facial recognition as a security measure. They can help keep track of patients who wander off and ensure no patient is left unaccounted for. This can also aid in detecting strangers that somehow make their way into the facility.
Face recognition technology is an integral part of our daily lives, and it is expected to stay that way in the near future, especially with real-time face recognition becoming more accurate and vital than ever. From the handful of facial recognition examples we looked at today, it became more than apparent that they are utilized in every corner of the world, from protecting our personal privacy in devices to helping track crime on a local and national level. It’s become easier and more accessible to take on creating your own face recognition software via machine learning now with the readily available datasets and libraries as long as you understand the fundamental components of the process, including face detection, alignment, measurement, and verification. We hope this guide to facial recognition will act as a foundation for your continuous learning!