Facial Recognition: How It Works, Its Benefits, Challenges, and Privacy Concerns

Humans are adept at recognizing faces, but we also interpret expressions and emotions quite naturally. Research says we can identify personally familiar faces within 380ms after presentation and 460ms for unfamiliar faces. However, this intrinsically human quality now has a competitor in artificial intelligence and Computer Vision. These pioneering technologies are helping develop solutions that recognize human faces more accurately and efficiently than ever.

These latest innovative and non-intrusive technologies have made life simpler and exciting. Face recognition technology has grown into a fast-developing technology. In 2020, the facial recognition market was valued at $3.8 billion, and the same is slated to double in size by 2025 – forecasted to be over $8.5 billion.

What is Facial Recognition?

Facial recognition technology maps facial features and helps identify a person based on the stored faceprint data. This biometric technology uses deep learning algorithms to compare the stored face print with the live image. Face detection software also compares captured images with a database of images to find a match.

Facial recognition has been used in many applications for enhancing security in airports, helps law enforcement agencies in detecting criminals, forensic analysis, and other surveillance systems.

How does facial recognition work?

Facial recognition software begins with facial recognition data collection and image processing using Computer Vision. The images undergo a high level of digital screening so that the computer can differentiate between a human face, a picture, a statue, or even a poster. By using machine learning, patterns and similarities in the dataset are identified. The ML algorithm identifies the face in any given image by recognizing facial feature patterns:

• The height to the width ratio of the face
• The color of the face
• The width of each feature – eyes, nose, mouth, and more.
• Distinctive features

As different faces have different features, so does facial recognition software. However, in general, any facial recognition works using the following procedure:

1. Facial detection

   Facial technology systems recognize and identify a facial image in a crowd or individually. Technological advancements have made it easier for the software to detect facial images even when there is a slight variation in posture – facing the camera or looking away from it.

2. Facial analysis

   Next is the analysis of the captured image. A face recognition system is used to accurately identify unique facial features such as the distance between eyes, length of the nose, space between mouth and nose, width of the forehead, the shape of the eyebrows, and other biometrical attributes.

   A human face’s distinct and recognizable features are called nodal points, and every human face has about 80 nodal points. By mapping the face, recognizing geometry, and photometry, it is possible to analyze and identify faces using the recognition databases accurately.

3. Image Conversion

   After capturing the image of a face, the analog information is converted into digital data based on the person’s biometrics features. Since machine learning algorithms only recognize numbers, converting the facial map into a mathematical formula becomes pertinent. This numerical representation of the face, also known as a faceprint, is then compared with a database of faces.

4. Finding a match

   The final step is comparing your face print with several databases of known faces. The technology tries to match your features with those in the database.

The matched image is usually returned with the name and address of the person. If such information is missing, the data saved in the database is used. 

[Also Read: How to Collect Data for Face Recognition]

Where Facial Recognition is Used?

Today, facial recognition systems are entering everyday life, and their use can frequently go unnoticed. To make life easier and add to safety, here are several prominent examples of facial recognition making a difference.

• Healthcare: Doctors use facial recognition to identify certain rare genetic disorders in children by skimming through facial features. An example of that would be the Face2Gene app, which compares a patient’s structure facially with known cases to help determine whether the child has Noonan syndrome or Angelman syndrome.
• Hotels: Some hotels are installing facial recognition to speed their check-ins. In China, the Marriott hotel lets guests enter a lobby kiosk for a quick facial scan, avoiding long lines at the front desk and making the entrance a pleasant affair.
• Accessibility: It allows visually impaired persons to authenticate themselves easily. No longer do they require passwords, PINs, or whatever else. With facial recognition, they can access banking apps or unlock devices, making daily tasks much more feasible.
• Classrooms: Apart from the security aspect, road schools are using facial recognition to monitor student engagement. For example, the systems can alert you on whether students are paying attention to the learning going on in class, allowing teachers to change their methods instantly.
• Event security: Facial recognition technology has found an application in the management of crowds and enhancing safety at large events like concerts and sports games. One example would be its deployment at stadium gates to verify ticket holders and prohibit unauthorized entry.
• Cars: Automakers are now integrating facial recognition into their cars for a better driving experience. Certain vehicles can recognize the driver’s face make automatic adjustments of seat positions and mirrors and even play specific playlists.

[Also Read: What is AI Image Recognition? How It Works & Examples]

What are the Pros of Facial Recognition

Facial recognition is a relatively new technology and offers multiple positives. Here are some pros of using facial recognition:

• Increased public safety: Police departments use facial recognition for identifying missing individuals and wanted criminals. For example, police departments in India have successfully brought lost children back to their families after matching their photos to missing-person databases.
• Secured transactions: Many banks and payment systems use facial recognition to make their transactions safer. For example, in Alipay, China, a user can authorize a payment simply by allowing their face to be scanned, hence reducing the event of fraud and providing convenience in cashless payments.
• Better healthcare: Hospitals have driven facial recognition systems to seamlessly access patients’ directories and speed up the registration process. Some systems even detect physical pain or emotional disturbances in patients, thus enabling doctors to provide better care.
• Security: Facial recognition technology has changed smartphone security forever. While Apple’s Face ID not only unlocks a phone, it also enables the protection of sensitive apps, such as digital wallets and banking apps.

Cons of Facial Recognition

It has certain advantages; however, more significantly, it raises ethical, privacy, and accuracy issues. Below are some of the drawbacks:

• Wrong accusation: Facial recognition systems may bring about wrongful accusations. The example of Randall Reid, who was arrested in 2022 based on the erroneous identification with DNA through facial recognition software for an offense in Louisiana, is in fact, a place he had never set foot in.
• Cultural and gender bias: Studies have shown facial recognition systems are less accurate in recognizing people of color and women. In a detailed report prepared for the US government regarding the performance of these systems, it was found that they routinely misidentified people from a minority background, leading to potential wrongful arrests or discrimination in law enforcement.
• Invasion of privacy: The place of facial recognition now raises ethical concerns because it collects and stores biometric data, sometimes without consent. As an example, some retail stores use facial recognition technology to track customer behavior, leading to concerns over surveillance and personal freedoms.
• The vulnerability of information security: The very act of storing facial data exposes one to hacking; as hackers have cracked sensitive biometric information, Black Hat hackers in just two minutes demonstrated that Apple’s face ID could be hacked.

[Also Read: 27 Free Image Datasets for Computer Vision]

Examples of Facial Recognition

• Amazon Recognition: The Amazon cloud-based facial recognition software has conducted law enforcement searches with the use of video footage to source people inside the body of a case. However, the company announced police will no longer be using it by 2020 while waiting for federal laws to be enacted protecting civil individuals in mind.
• Apple Face ID: Apple implements facial recognition systems on its devices allowing users to unlock their phones, log into their apps, and make purchases safely; a complete standard for convenience and security in consumer electronics.
• Facebook (Meta): In 2010, Facebook launched facial recognition technology for tagging photos. The ability to use such technology is optional, and it allows automatic tagging of friends after uploading photos, as they have been recognized in the photos themselves.
• Google Photos: Google employs facial recognition for organizing and automatically tagging images, which makes it easier for users to track and find images with recognized faces.
• Snapchat: A pioneer of facial recognition software, Snapchat utilizes such technology for its popular unusual filters for various objects and sporting personalities.