Facial recognition is a process of using computer vision based mathematics to detect and recognize a human face in a photograph or video. Using biometrics the facial recognition system maps facial features such as the location and shape of the eye, nose, mouth, distinguishable landmarks unique to the person and other geometric aspects to the face.
Complex mathematical algorithms are used to produce a numerical sequence that represents the face in a language a computer can understand. This “faceprint” is as unique as a human fingerprint and can be analyzed in real time to identify people as they walk past a video CCT security camera.
The facial recognition industry is expected to grow to over $7.7 billion annually in 2022, nearly doubling from $4 billion in 2017.
For privacy-minded individuals, you will want to pay close attention to how and by whom your faceprint data is used. The technology itself is not dangerous and people do not need to be paranoid about “big brother watching you.” Cautious? Yes, but paranoid about facial recognition technology, no.
Just like all technology, those that are criminally minded can abuse facial recognition. Luckily there are also good uses
In the past, most systems required the person to face the camera and have their face correctly framed within the photo. This may work for mug shots taken by the local Sheriff’s Office, but for real-time analysis of CCT security camera footage people as they walk by, newer technology needed to be invented.
Now, many systems can build a 3D representation of the human face based upon multiple photos known to be of that individual, giving the system a more detailed understanding of that person’s unique facial attributes.
All uses of this technology, for good and evil, requires advanced neural networks trained to work together to provide the user with the desired service.
Article Quick Links
- How Facial Recognition Works
- Law Enforcement Use of Facial Recognition
- Bringing Facial Recognition to the Masses
Breaking Down the Steps
Humans are great at recognizing people’s faces; we have had more than 2 million years of evolutionary adaptation behind us. Computers, even with all of their power cannot natively look at a person’s face and say “That’s Aunt Betty.”
Computer vision research started in the 1960s with scientists teaching computers how to detect and recognize a human face. Computers have come a long way since the 60s, but they still must be trained what to look at and how to distill that information down to binary computer language of 1s and 0s.
Before a computer can recognize a face in a photo, it must first determine if a human face is even in the picture. Facial recognition is very expensive in terms of computing power, whereas simple ‘is there a person in the photo’ is very inexpensive. For this reason, systems elect first to verify if there is a person in the photo before attempting the more complicated matching the photo to an actual person’s identity. This process is called Face Detection
1) Face Detection
A photo is analyzed to find the human face(s) within the image. Each face detected is isolated and processed separately. Open source libraries such as OpenCV and dlib are standard; of course, there are also many proprietary systems in use as well.
The face is cropped from the original picture with the eyes and mouth aligned in a common location for all photos. With these attributes in the same general area for all images, it makes the next steps easier for the computer.
The facial recognition software will then generate a unique and propriety identifier for this face based upon the facial landmarks detected. This identifier is usually represented by a series of floating point integers (computer geek speak for a number with a decimal point). In this step, the system detects and extracts all of the meaningful data that it will use to generate your faceprint.
Of course, these numbers do not really mean much to us as mere mortals, to a computer this is what a human face looks like. Each number in the array is a numerical representation showing the location of the eyes, nose, mouth, etc.
With this unique identifier, the facial recognition software will then query a database of hundreds, thousands, or even millions of known identities and return a list of possible matches.
This query to the database is performed by comparing the Euclidean distance between the positions of each array element’s floating point element compared to that of the same element of a different person’s array. If the Euclidean distance of all 128 points is less than 0.6 it is a strong candidate for a match.
Depending on how powerful the system is, in most cases the above four steps are processed and returned to the user in milliseconds. Now a human can manually verify the results returned by the system.
In our example, the girl from ‘Face 1’ is already known to the database and returns her information:
We have covered the basics of how to detect a face in a photo and searched our database to see if that person is known, but we skipped a very important step.
Before you can run Face Detection or Face Recognition, you must create and train computer vision models so that the computer knows what a human face “looks like.” There are some great python wrappers around dlib like face_recognition that are open source and are already trained. Or if you are more adventurous, you can train your own models, I will direct you to an excellent article by Dr. Adrian Rosebrock on how to train a facial recognition model.
An essential piece of a facial recognition system is the ability to intelligently group photographs of the same person into groups, or clusters. This can be accomplished through manual, semi-automated, and fully automated processes. By clustering these photos together, the system can better handle occlusion issues that partially block parts of the face in an individual photo such as sunglasses, hairstyles that cover part of the face, etc.
Clustering can also help by grouping photos of a person from dozens of different angles giving a complete representation of the person’s unique facial structure in 3-dimensional space.
Law Enforcement Use of Facial Recognition
MorphoTrust Idemia, is one of the largest vendors of face recognition and other biometric identification technology in the United States. It has designed systems for state DMVs, federal and state law enforcement agencies, border control and airports (including TSA PreCheck), and the state department.
Back in the day police departments had to manually search filing cabinets and mug books filled with photographs of suspects. Now they can use smartphone apps to take a quick photo of a suspect and instantly have their criminal records and personal information displayed on the screen for their review.
Many of these systems work together with social networking and other integrated surveillance systems to provide a complete look into a person’s identity.
Tech services that assist law enforcement and private sector litigators to search for and combat online revenge porn uploads from ex-boyfriends and ex-girlfriends.
Battling Child Porn
While not technically “Facial Recognition” per se, companies like Microsoft use computer vision algorithms to detect known child pornography online. They offer a free service called PhotoDNA to law enforcement agencies.
PhotoDNA creates a unique digital signature (known as a “hash”) of an image which is then compared against signatures (hashes) of other photos to find copies of the same image. When matched with a database containing hashes of previously identified illegal images, PhotoDNA is an incredible tool to help detect, disrupt and report the distribution of child exploitation material. PhotoDNA is not facial recognition software and cannot be used to identify a person or object in an image. A PhotoDNA hash is not reversible, and therefore cannot be used to recreate an image.Microsoft, PhotoDNA (https://www.microsoft.com/en-us/PhotoDNA)
Bringing Facial Recognition to the Masses
This face recognition technology is used in more products and services that you may realize
Online/Mobile Device Banking
Many national banks like Wells Fargo and Bank of America are using facial recognition in their mobile apps and in their ATMs to better verify and authenticate legitimate users