AI Facial Recognition: How Does It Work?

AI facial recognition uses advanced neural networks to convert images into numerical data and identify patterns. Numerous industries are using AI facial recognition today, including healthcare and security. So how do these algorithms process facial features? 

How AI Facial Recognition Works

AI facial recognition applies the neural network capabilities of computer vision machine learning to identify facial features. Computer vision or image recognition algorithms are trained to distinguish certain types of images or features from others. 

One specific type of neural network often used in facial recognition is convolutional neural networks (CNNs). These algorithms use mathematics and image data to determine the content of an image. As the photo is passed through three or more layers, the neural network converts the image into numerical data and then gradually simplifies that data. The final layer of the neural network, the fully connected layer, eventually outputs a category or classification for the image. 

There are a variety of ways developers can train a CNN algorithm. For instance, they might have the algorithm process images of objects, animals, and humans and allow the algorithm to classify the photos themselves. They could also label the images and reinforce correct categorization. Regardless of the specific training method, developers will usually go through multiple testing phases so the algorithm can more accurately classify the images it processes. 

Neural Networks in AI Facial Recognition

CNN neural networks are commonly used in facial recognition AI algorithms. In this case, the neural network would be trained to identify the various features of the human face, such as eyes, ears, mouth, and nose. Therefore, one of the main advantages of applying CNN AI to facial recognition is the processing capabilities of neural networks. 

Neural networks are built to mimic the way a human brain functions. They’re made up of interconnected “nodes” that work together to process information. A neural network is capable of accurately handling much more data than a non-intelligent software program. It can be trained with larger data sets, tuned with greater precision, and analyze higher quality images. CNNs can perform these tasks with less computing power than older iterations of neural network technology. 

These features mean AI facial recognition is both more capable and more accessible today. In addition, CNN algorithms can process higher-quality image data, which supports more detailed, accurate facial recognition. 

Applications of AI Facial Recognition

How are high-performance AI facial recognition algorithms used today? There are still some challenges for this technology to overcome. However, a few applications are already proving useful. 

Biometric Security

Millions of people use biometric security methods every day. Fingerprint recognition, such as that found on many smartphones, is one of the most common biometric authentication methods. However, facial recognition is on the rise. More and more consumer devices are offering facial recognition security features. For instance, Apple’s newest iPhone and iPad models have Face ID, which uses AI facial recognition. 

Biometric security has many applications beyond consumer electronics. For example, organizations and businesses can use AI facial recognition in their security cameras. The CNN algorithm could be trained to signal an alert if it detects unauthorized personnel on security footage. Due to the high efficiency of CNN algorithms, the AI facial recognition program could process security footage at or near real-time speeds. 

Medicine

Many medical conditions trigger changes in patients’ physical features. AI facial recognition can be used to identify and diagnose diseases and conditions using symptoms present in facial features. While a medical professional is still responsible for the final diagnosis and treatment, facial recognition AI can speed up the screening process. 

Studies have already found up to 97% accuracy in disease diagnosis using AI facial recognition. However, accuracy still varies depending on traits like race, gender, and age. Further development aims to improve algorithms’ performance. Nevertheless, doctors may soon be able to use AI facial recognition to quickly diagnose patients so they can get the treatment they need in less time. 

Law Enforcement

The most well-known application for AI facial recognition is law enforcement. However, there is some controversy around this application, largely due to varying degrees of accuracy in some AI algorithms and how it is applied.

For example, an algorithm might identify the faces of people of color less accurate than those of white people. Algorithms can also be guilty of data bias or logic bias, which occurs when an AI accidentally adopts underlying biases present in training data. This means the AI makes a connection that mimics inherent biases humans are practicing, such as racism. As a result, rather than classify data objectively, the AI ends up classifying data with unfair weights and biases. 

This is due to the AI black box, the inaccessible part of AI algorithms where their logic and processing occur. In conventional black box AI, developers can’t see how the AI is coming to its conclusions. So, if logic bias occurs, the developers might never know until the AI has been in use for a long time. 

Data bias and uneven accuracy are serious concerns in law enforcement AI facial recognition. An error in these algorithms could have serious consequences in people’s lives. For example, an innocent person could be incorrectly identified as a suspect while the real perpetrator gets away. 

Any AI facial recognition algorithms used in law enforcement need to be held to the highest standards of precision and accuracy across all demographics. However, with more development and innovation, neural networks could be highly effective for law enforcement applications. 

Applying AI to Facial Recognition

AI technologies like machine learning and neural networks are perfect for improving the accuracy and precision of facial recognition. For example, CNNs allow higher-quality images to be processed with greater speed and accuracy while requiring less processing power. This makes AI facial recognition more functional and accessible. Developers are already applying it in a variety of fields, from consumer electronics to life-saving medicine.