Few biometric technologies get as many responses – positive or negative – as facial recognition. When combined with artificial intelligence, facial recognition is highly accurate but can be considered invasive. Today, we’ll look at how AI is incorporated in facial recognition technology and what it means.
WHAT IS AI FACE RECOGNITION?
Facial recognition technology is a set of algorithms that work together to identify people in a video or still image. This technology has been around for decades, but it has become much more prevalent and innovative in recent years.
One of those innovations is the integration of artificial intelligence (AI) in facial recognition systems. AI-based intelligent software can instantly search a database of faces and compare them to one or more faces detected in a scene. Instantly, you can get highly accurate results – typically, systems provide 99.5% accuracy rates on publicly available standard datasets.
AI facial recognition software has the following advantages:
- Real-time identification;
- Anti-counterfeiting measures;
- Reduce racial or gender bias due to model training on millions of faces;
- Can be used on multiple cameras.
WHAT IS AI?
Artificial intelligence (AI) is a broad subset of computer science that revolves around the development of intelligent machines that can perform tasks that would normally require some human intelligence. It is a multifaceted, interdisciplinary science, but modern advances in deep learning and machine learning are bringing it into almost every area of the tech industry.
WHAT IS DEEP LEARNING?
Deep learning is a function of AI; it mimics the processing and patterning capabilities of the human brain and uses those capabilities 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 unsupervised. Deep learning is also known as “deep neural networks” or “deep neural learning”.
HOW AI FACIAL RECOGNITION WORKS?
The basic way that AI in facial recognition works is that you start with a tagged set of features. Essentially, you’re starting with photos that have existing correlations, hand-matched with relevant people. There needs to be an initial, manual correlation between a person’s face and the rest of their identity. And once that started, it became easier to identify faces in photos of people “in the wild” — so to speak, where indistinct photos were matched to that dataset.
And how, exactly, can AI recognize faces? Well, each person’s face is divided into multiple data points; they could be the distance between the eyes, the height of the cheekbones, the distance between the eyes and the mouth, etc. Face recognition AI looks on those data points and tries to explain the variations (eg interval distance from the camera and slight changes in the angle of the face).
However, even well-trained AI facial recognition systems have no real-world context and can be fooled. If you see a colleague wearing a mask, sunglasses, and cap, you can still recognize them. However, an AI system may not. It depends on the neural network training level. While AI facial recognition systems have more superficial accuracy, they are also more prone to mistakes under less-than-ideal conditions.
WHERE IS FACE RECOGNITION AI USED TODAY?
AI facial recognition is applied to many industries today. For example:
- Health care: Computer vision is combined with AI to support pain management processes and track patient medication consumption.
- Guard: Deep learning algorithms are helping to reduce the need for regular password input on mobile devices, identify fraud detection, and improve anti-spoofing.
- Boarding: Every year, more than 100,000,000 people pass through Paris’s Orly and Charles de Gaulle airports. To speed things up, airports have begun using “smart gates,” which use a combination of facial recognition and liveness testing.
- Surveillance: Some monitoring services use AI solutions to detect and record suspicious behavior through webcam monitoring. The live presenter can then analyze and contextualize those events.
HOW ARTIFICIAL INTELLIGENCE IS TRAINED FOR FACIAL RECOGNITION
As mentioned earlier in the article, AI face recognition needs practice on manually selected sets of images. Some companies make this easier for AI developers by providing training data for facial recognition systems, facial recognition models see multiple calculations instead of a single human face.
For security and monitoring purposes, a model can compare those calculations with other face computations located in the database. However, regardless of the use case, every AI facial recognition system needs training with a lot of facial image data. AI models have to be trained with facial images that vary in ethnicity, age, angle, lighting, and other factors.
Sometimes, to build their training datasets, facial recognition companies use the open web to collect photos of people without consent. This is controversial, and its morality is called into question – we’ll take a closer look at it in the next section.
WHAT IS THE PROBLEM WITH AI FACIAL RECOGNITION?
As explained in this article from Nature.com, there are many ethical issues involved in the development of AI facial recognition. For example, researchers at Harrisburg University, PA, have developed AI facial recognition software that, in their words, can predict whether someone will turn out to be a criminal with 80% accuracy. There was a wave of negative reaction, and Harrisburg eventually removed their press release on the subject and did not publish the work.
Another sticking point is the collection of data without consent. Until the early 2000s, AI developers often had volunteers to do the training data. Today, however, the majority of facial images are collected without permission. For example, in 2016, researchers from Seattle’s University of Washington posted a database containing 3.3 million face photos cropped from Flickr without consent. Currently, there are no clear legal protections regarding the collection of facial recognition training data – but recently, Facebook paid out $650 million for facial data collection.
Some companies, such as Google, have publicly stated that they are taking a more responsible approach to face-related technologies. Some of the standards include:
- Does not amplify or reinforce existing biases;
- Do not use these technologies in ways that violate internationally accepted ethical standards;
- Protect privacy by providing the ideal level of control and transparency.
WHAT PROGRAMMING LANGUAGE IS USED TO MAKE AI FOR FACIAL RECOGNITION?
By using deep learning, NamiQ has built a face recognition model, and trained that model with more than one million face images. We use Python API on top of C++ engine to speed up the inference process. The model is later deployed on docker and served by Restful API which make it very easy to scale and can be used to recognize hundreds of people in one second when combine with an appropriate clustering algorithm.
The FUTURE of AI FOR FACE RECOGNITION
The more sophisticated and intelligent the facial recognition feature is, the harder it is to understand how it actually works. Neural network reasoning is integrated into the behavior of thousands of “neurons,” which are combined into hundreds of interconnected layers.
In the years to come, America will need to make tough choices about AI: individuals like Stephen Hawking and Elon Musk have voiced hesitation about using AI, arguing that it could end in the destruction of AI. mankind.
However, some countries are taking the lead in AI facial recognition; Currently, China is leading this industry. China’s goal is to set industry standards now, so they can join hands in shaping the development and implementation of standards around the world. As the technology battleground between the US and China intensifies, we are sure to see more and more AI solutions and standards being developed at a rapid pace.
SUMMARY
AI facial recognition is powerful, but it comes with a host of ethical implications. What do you think? Is it possible to adapt the way facial data is collected for AI systems? And, if possible, that means it’s necessary. These are tough questions, but we’ll keep you updated as more legal precedents become available and as the facial recognition industry continues to evolve.
FACE RECOGNITION AI FAQ
WHAT IS AI FACIAL RECOGNITION?
AI-based intelligent facial recognition technology is software that can instantly search a database of faces and compare them with one or more detected faces in a scene.
HOW DOES AI FACIAL RECOGNITION WORK?
Each person’s face is divided into multiple data points; they could be the distance between the eyes, the height of the cheekbones, the distance between the eyes and the mouth, etc. Face recognition AI looks on those data points and tries to explain the variations.
DOES FACIAL RECOGNITION USE AI?
Yes, most modern face recognition algorithms have some similarities of built-in deep learning and neural networks.
HOW CAN AI BENEFIT BIOMETRICS?
AI biometrics can reduce authentication and identity verification costs, respond flexibly to fraud threats, and deliver enhanced accuracy, speed, and scalability.
