Id-Me platform is a software product that includes the features of industry solutions for creating a united biometric landscape
Specially designed biometric identification plugin for CCTV, access control and VideoXpert automation systems created by Pelco
Software product that provides reliable staff monitoring hours through biometric identification
Software product that provides biometric access to banking operations conducted through ATMs
Software product biometric facial identification for security purposes with using video stream
Software product for enriching the biometric capabilities of access control systems
Software product that provides an increased level of targeting by employing biometric identification
Software product that uses biometric identification to display personalized welcome messages on interactive screens and monitors
Software product developed to expand functions of the traditional credit conveyor, and increase the quality of clients’ inspection through biometric verification
Reliable and trustworthy biometric control access to operating and information systems
Software product, which expands the toolset of the electronic queue terminal through biometric identification
Software product that provides a reliable and quick check of the gym clients access right without employee’s participation
A new level of work with visitors and employees of Business centers opened with the help of biometric products.
Biometrics for convenient service to citizens, including remote monitoring of the quality of personnel work.
Biometric monitoring of working hours and additional security tools for industrial facilities.
Modern methods of biometric analytics for safe operation of sports facilities.
Convenient and secure transport solutions based on the digital identity of the passenger.
Biometric solutions for a new level of security and interaction with visitors.
Biometric video Analytics for targeted marketing and personnel control in distributed networks.
Biometric products for proctoring and video surveillance systems in educational institutions.
Keyless biometric access to rooms, targeted approach to each client and information about the time of work for employees.
Necessary tool for the security and competitiveness of a modern Bank.
Improving the level of security, speed of investigations and timely prevention of illegal acts in the urban public space.
Customer-oriented solutions, acceleration of the work process of the registry area, control of the staff of the entire institution.
RecFaces is a developer of enterprise-level multimodal biometric identification application solutions customized to the specific, current, and future needs of various industries.
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What Is AI Facial Recognition Tech and How does It Work?

What Is AI Facial Recognition Tech and How does It Work?

Few biometric technologies have received as many reactions – positive or negative – as facial recognition. When combined with artificial intelligence, face recognition is highly accurate but can be considered invasive. Today, we’ll take a look at how AI is incorporated in facial recognition technology and what its implications are.

Table of Contents

What Is AI Face Recognition?
What Is AI?
What Is Deep Learning?
How AI Facial Recognition Works
Where Is Face Recognition AI Used Today?
How Artificial Intelligence Is Trained for Facial Recognition
What Is the Problem With AI Facial Recognition?
What Programming Language Is Used to Make AI for Facial Recognition?
Future of Artificial Intelligence for Facial Recognition
Summary
Face Recognition AI FAQ
What is AI facial recognition?
How does AI facial recognition work?
Does facial recognition use AI?
How can AI benefit biometrics?

What Is AI Face Recognition?

Facial recognition technology is a set of algorithms that work together to identify people in a video or a static image. This technology has existed for decades, but it has become much more prevalent and innovative in recent years.

One such innovation is the integration of artificial intelligence (AI) within facial recognition systems. Intelligent, AI-based software can instantaneously search databases of faces and compare them to one or multiple faces that are detected in a scene. In an instant, you can get highly accurate results – typically, systems deliver 99.5% accuracy rates on public standard data sets.

AI face recognition software has the following advantages:

  • Real-time identification;
  • Anti-spoofing measures;
  • Lessened racial or gender bias due to model training across millions of faces;
  • Can be used across multiple cameras.

What Is AI?

Artificial Intelligence (AI) is a vast subset of computer science revolving around the development of smart machines that can perform tasks that typically need some semblance of human intelligence. It is a multi-faceted, interdisciplinary science, but modern advancements in deep learning and machine learning are bringing it into nearly every area of the tech industry.

What Is Deep Learning?

Deep learning is a function of AI; it imitates the processing power and pattern-creation capabilities of the human brain and uses those abilities to make decisions. Deep learning is a subset of AI’s machine learning, and it has networks that can learn from unstructured or unlabeled data – and it can do so without supervision. Deep learning is also referred to as a “deep neural network” or “deep neural learning.”

How AI Facial Recognition Works

The basic way that AI in facial recognition works is that you begin with a tagged feature set. Essentially, you are starting with photos that have existing, hand-matched correlations to the people involved. There needs to be an initial, manual correlation between a person’s face and the rest of their identity. And once that gets started, it becomes steadily easier to identify faces in pictures of people “in the wild” – so to speak, in which pictures that aren’t as clear are matched to that data set.

And how, exactly, is AI able to recognize faces? Well, each person’s face is broken up into numerous data points; these can be the distance between the eyes, the height of the cheekbones, the distance between the eyes and the mouth, and so on. AI facial recognition searches on those data points and tries to account for variations (for instance, distance from the camera and slight variations in the angle of the face).

However, even well-trained AI facial recognition systems don’t have real-world context and can be fooled. If you see a colleague who is wearing a face mask, sunglasses, and a baseball cap, you may still recognize them. An AI system, however, might not. It depends on level of training the neural network. Even though AI facial recognition systems are more superficially accurate, it is also easier for them to blunder under less-than-ideal conditions.

For a deeper look at how AI facial recognition works, check out this video from IDG TECHtalk.
https://www.youtube.com/watch?v=aLkSq8SEOnU

Where Is Face Recognition AI Used Today?

Face recognition AI is applied to many industries nowadays. For instance:

  • Health care. Computer vision is combined with AI to support pain management procedures and track patient medication consumption.
  • Security. Deep learning algorithms are helping to reduce the need for regular passwords on mobile devices, recognize fraud detection, and improve anti-spoofing capabilities.
  • Airport boarding: Each year, over 100,000,000 people pass through Paris’ Orly and Charles de Gaulle airports. To speed things up, the airports have begun using “smart gates,” which use a combination of facial identification and liveliness checks.
  • Proctoring: Some proctor services use AI solutions to detect and document suspicious behavior via webcam monitoring. Live proctors can then analyze and contextualize those events.

For more examples of how face recognition AI is used, read our article on the topic.

How Artificial Intelligence Is Trained for Facial Recognition

As mentioned earlier in the article, AI face recognition needs to practice on manually selected photosets. Some companies make this easier for AI developers by providing training data for facial recognition systems. Let’s look at Lionbridge AI, for instance. According to them, facial recognition models see many calculations instead of a human face.

For security and surveillance purposes, a model can compare those calculations to other face calculations located within a database. But, regardless of the use case, every single AI facial recognition system needs to train with lots of face image data. AI models must be trained with facial images that vary in ethnicity, age, angles, lighting, and other factors.

Sometimes, to build their training datasets, facial recognition companies scrape the open web to gather photos of people without consent. This is highly controversial, and the ethicality of it is brought into question – which we’ll take a closer look at next.

What Is the Problem With AI Facial Recognition?

As explained in this article from Nature.com, there are plenty of questionable ethics involved with the development of AI facial recognition. For instance, researchers at Harrisburg University, PA, developed AI facial recognition software that, in their words, could predict whether somebody was going to become a criminal and with 80% accuracy. There was a wave of negative reactions, and Harrisburg ended up removing their press release on the topic and did not publish the work.

Another sticky point is the collection of data without consent. Up until the early 2000s, AI developers typically got volunteers to pose for training data. Nowadays, though, the majority of facial images are collected without permission. For instance, in 2016, researchers from Seattle’s University of Washington posted a database that contained 3.3 million photos of faces scraped from Flickr without consent. Currently, there are no clear legal safeguards regarding the gathering of facial recognition training data – but, recently, Facebook paid a $650 million settlement for harvesting facial data.

Some companies, such as Google, have publicly proclaimed that they are taking a more responsible approach to face-related technologies. Some standards include:

  • Not amplifying or reinforcing existing biases;
  • Not using these technologies in ways that violated internationally-accepted ethical norms;
  • Protecting privacy by providing an ideal level of control and transparency.

What Programming Language Is Used to Make AI for Facial Recognition?

RecFaces has a flexible ecosystem of tools, libraries, and community resources. This allows researchers to leverage the latest biometric technologies. Recently, artificial intelligence has been increasingly used, which expands the familiar framework of security systems and becomes an indispensable tool for quickly responding to various threats.

Many techniques are used to implement facial recognition algorithms and AI makes algorithms more and more efficient year by year. An effective face recognition system can improved using deep learning (part of artificial intelligence) by providing sufficient data. Like most deep learning frameworks, RecFaces uses a Python API on top of a C and C ++ engine to speed up the AI facial recognition. Our Id-Me technology platform contains more than ten highly effective products and can recognize a person by biometrics in 1 second.

RecFaces specialists are constantly working on improving equipment and introducing the latest technologies. Our developers work in OpenCV, MATLAB, Python, and Java as some of the most widely used languages. These technologies stand out in terms of speed and efficiency, expanding machine learning capabilities.

Future of Artificial Intelligence for Facial Recognition

The more complex and intelligent that facial recognition becomes, the harder it is to understand how it actually works. A neural network’s reasoning is integrated into the behavior of thousands of “neurons,” which are combined into hundreds of interconnected layers.

In the coming years, the US will need to make difficult choices about AI: individuals such as Stephen Hawking and Elon Musk have voiced their hesitancy on using AI, suggesting that it could end up destroying humanity.

Yet, some countries are barging ahead in the AI facial recognition realm; currently, China is leading the industry. China’s goal is to establish industrial standards now, so that they can have a hand in shaping the development and implementation of worldwide standards. As the technological battleground between the US and China intensifies, we are sure to see more and more AI solutions and standards developed at a rapid rate.

Summary

AI facial recognition is powerful, but it comes with a large set of ethical implications. What do you think? Is it possible to regulate the way that facial data for AI systems is harvested? And, if it’s possible, does that mean that it’s necessary. These are tricky questions, but we will keep you updated as more legal precedents are set, and as the facial recognition industry continues to evolve.

Face Recognition AI FAQ

What is AI facial recognition?

Intelligent, AI-based facial recognition technology is software that can instantaneously search databases of faces and compare them to one or multiple faces that are detected in a scene.

How does AI facial recognition work?

Each person’s face is broken up into numerous datapoints; these can be the distance between the eyes, the height of the cheekbones, the distance between the eyes and the mouth, and so on. AI facial recognition searches on those datapoints and tries to account for variations.

Does facial recognition use AI?

Yes, the majority of modern facial recognition algorithms have some semblance of integrated deep learning and neural network.

How can AI benefit biometrics?

AI biometrics can reduce costs of identity authentication and verification, flexibly respond to fraud threat, and deliver enhanced accuracy, speed, and scalability.