Here’s a fact to surprise you: face recognition technology first emerged over 50 years ago. Back in the 1960s, a research team utilized a basic scanner and computer to map biometric data like the location of the nose and eyes. Though it was a breakthrough, for the next few decades, computers’ work was more focused on beating a human opponent in chess rather than analyzing and recognizing human faces.
Today, the face recognition (FR) industry is expected to reach 3.8 billion USD by 2025. However, many still believe this immense popularity is a huge mistake. So, what’s the problem with using facial recognition software?
How Does Facial Recognition Software Work?
Let’s start by reviewing the facial recognition software definition. Facial recognition software is a special type of software that can help a computer recognize a person’s digital image. Traditionally, its workflow comprises three core steps: detection, analysis, and recognition. However, this scheme is rather general, which means that AI facial recognition software utilizes proprietary algorithms.
Detection
The camera discovers a face in an image. Like allowing an inbuilt camera to auto-focus, thus highlighting faces in the crowd and drawing a box around them, FR software uses computer vision to locate faces faster and more accurately than the human eye. By applying Artificial Intelligence technology, FR systems extract certain information and analyze and classify it from image data, camera angles, video footage, and more.
Analysis
Once a face is located, facial recognition software for photos automatically measures landmarks (the depth of eye sockets, the distance between eyes, the contours of ears, chin, lips, and the shape of the cheekbones) and instantly transforms this data into points and numbers. This string is called the faceprint and is similar to fingerprints because every person has a unique print.
Recognition
The biometric face recognition software then cross-references the face to a database of other faces to confirm a user’s identity. The best facial recognition software can calculate a confidence score that measures how alike two images are. This score gauges probability prediction. The higher this score. The greater the probability that images match.
Some AI face ID software types will offer a categorization feature. This is when algorithms do not confirm one’s identity. Instead, the algorithms label a user as a person belonging to a specific age or gender group.
Where Facial Recognition Is Applied?
If you still think that photo facial recognition software has a narrow use area, you’re so wrong. It’s actively being used.
Organizing Photos
Mobile devices on iOS and Android can organize library photographs automatically by applying facial recognition technology. The Apple Photos app calls this feature People & Pets, while the Google Photos app calls it the face grouping feature. Organizing images in a personal library is fast, easy, and convenient with either app.
Accessing Transportation Services
Did you know that the TSA (Transportation Authority of Security) utilizes software for facial recognition (including different scans) at airports? They apply the software to quickly and effectively match passengers to their identification documents. This is a relatively new way to access transportation, but it has already proven its efficiency.
Accessing Personal Devices
Apple’s latest mobile phones utilize facial recognition, too, allowing users to log in or access different features on a device (Apple Wallet) without a password or PIN. According to Apple, there is only a 1 in 1,000,000 chance that an unauthorized person can unlock a phone with the Face ID feature activated.
Verifying for Online Apps
Today, there is a long list of online applications that are already using facial recognition technology. One of the brightest examples is the DoorDash food delivery platform. Today, it requests delivery service drivers to identify via a selfie before they complete a delivery.
Helping in Medical Treatment
FR is applied when patients and doctors are looking for ways to detect illnesses before it’s too late. For instance, the Face2Gene software is an app that assists physicians with genetic disorder diagnoses. Face2Gene applies Artificial Intelligence and Deep Learning algorithms to match the frequent features of diverse genetic disorders.
Filing Taxes
In the past, the IRS used facial emotion recognition software to identify taxpayers’ online accounts. However, since they were using third-party software instead of custom-made software, there were privacy concerns, so the IRS discontinued this option.
Entering Hotel Rooms and Apartments
Keyless hotel room entry is gaining popularity across geographies. The hotel industry now uses facial recognition software for automated check-ins. One example is the Vinpearl Nha Trang hotel chain, which has already adopted FR-powered check-in systems.
Targeting and Selling Products
Most consider this use case a rather unexpected turn. However, FR software is used to target and sell goods and services to buyers based on the information expressed by their faces. Walgreens, for instance, has set up coolers with digital displays in its stores. All coolers have FR camera software, and after detecting a buyer’s sex and age, it shows buyers customized ads.
Finding Missing People
FR systems are paired with street surveillance and traffic cameras for law enforcement use. They are so accurate that they can pick a single face out of hundreds of people in a crowd. Non-profit organizations, together with the International Network of Associations of Disappeared Persons, are now recruiting people to find photos of missing people on their smartphones with the assistance of FR technologies.
Finally, this technology proved its extreme efficacy right after the COVID-19 outbreak. Since early 2020, facial recognition tools have been used to ensure contractless authentication and entry. This helped reduce touchpoints and ensure entrants followed all crisis protocols.
Technologies of Top Facial Recognition Software and Their Accuracy
Technologies at the core of image and video facial recognition software are responsible for the system’s sophisticated ability to distinguish real faces from images and videos by locating the face, examining its main features, and deriving them to create the faceprint:
- Artificial Intelligence: This is a combination of Artificial Intelligence, Machine Learning, and Deep Learning algorithms that, working as a whole, enable the software to learn from datasets and learn.
- Computer Vision: This is the area of AI focused on how well a computer understands images and videos. The process covers automatic detection, analysis, and comprehension of useful image or video data.
- Biometric Analysis: Biometrics is the static investigation of one’s biological information, aka unique facial features.
- Neural Networks: This is all about convolutional neural networks, which are used in image recognition and indispensable to advanced facial recognition software.
When put together, these are the cornerstones of instruments for facial recognition that boast the highest levels of reliability and accuracy.
But how accurate is the best facial recognition software for photos or videos? It’s a fact that the top identification algorithm was recently considerably improved and now has an error rate as low as 0.8%, meaning that a reliable system achieves an accuracy score as high as 99.97%.
Facial Recognition Software Pros and Cons
From what you’ve already read in the use cases section, it’s clear that the benefits of facial expression recognition software are numerous and can be used across industries and geographies. Below, we have defined the core ones:
The list of advantages of facial recognition software can go on and on. But there are also disadvantages to consider.
Racial Bias
Since 2023, FR algorithms have guaranteed a high level of classification accuracy (over 90%), yet these numbers aren’t universal. In cases with dark-skinned faces, the number of errors in a face recognition system grows, which is unfortunately not the case with light-skinned faces. This means that even the most superior technologies are likely to show bias toward men of color.
Law Enforcement
In 2019, the U.S. federal government shared a report confirming the detection of discrimination issues in their FR algorithms. According to the report published, their algorithms were efficient in the cases of the faces of middle-aged white males. Yet, they were underperforming in cases of people of color, women, children, and the elderly. This triggered lengthy incarcerations, violence, and wrongful arrests.
Data Privacy
There’s still no evidence of how data is collected and stored when FR software is used, which is why the privacy of user data has already become a major concern. The ongoing and active implementation of facial recognition systems equals ongoing government surveillance and the storage of images without user consent. This is already an issue in the USA and some European countries, triggering protests and strict regulations. Eventually, this can give users more control over their data usage.
Data Breaches
So far, there hasn’t been any significant data breach directly related to facial recognition software. However, the possibility has raised concerns for the governmental institutions and the public. At the same time, alongside an advance in the development of AI-powered FR technologies, major steps were made to advance cloud-based storage cybersecurity.
How Thieves Try to Surpass FR?
On their side, criminals are also advancing their knowledge of facial recognition tools. During the process of customer onboarding, they create fraudulent user profiles and try to bypass security measures. With this in mind, they’re following two typical routes:
- They deceive facial recognition systems by utilizing digital and/or printed images and sometimes videos of account holders. By attacking, they want to trick the financial platform into recognizing fake photos and videos as genuine faces. If they succeed, their next step is to create fraudulent accounts.
- They utilize deep fake technology to create realistic photographs and videos that imitate someone’s face. Deepfakes allow criminals to trick FR systems by showing a synthetic/manipulated face that looks like a real individual.
For this reason, businesses are implementing multifactor authentication, combining diverse verification methods with facial recognition and additional biometric solutions. This allows them to mitigate risks. Additionally, companies are investing time and effort into regular FR algorithm updates, thus predicting more sophisticated attacks.
What Is Facial Recognition Technology Future?
2024 is already known for the emerging FR technology trends. But what will the future bring? There’s hardly a person able to give you a 100% accurate reply. However, at LITSLINK, we’ve investigated the domain, and here’s the list of potential trends for 2025-2027:
We expect that this list will grow soon.
Summing Up
Any new technology or trend has two sides. Disadvantages are the red flags that show why businesses and users should avoid facial recognition software. But, as the flow of human history shows, the drawbacks are the impetus for development. Will it be the same with facial recognition software? It certainly will.
Let’s explore more opportunities the FR systems can bring into your business processes: leave us a message, and we’ll get back to you with a call offer. Together, we’ll bring your business to new heights.