Going Back to Basics: The Fundamentals of Image Recognition

We often talk here at Facia about the many benefits of facial recognition, and biometric security systems. We go into a lot of detail about the way they work, their many benefits in different applications, and the many conversations and ideas that surround the tech.

But do you know what the underlying technology is that enables and powers such systems in the first place? You have probably heard of us talking about AI. Well, Image recognition is the AI/machine learning tech that is the basis on which all forms of facial recognition and its derivatives work.

So for today, let’s revise and refresh our fundamentals, and learn how the foundational process underneath face biometrics works.

So What is Image Recognition?

Let’s keep facial recognition as the basis for our explanation. The very first step in the facial recognition process is face detection (where the system detects the presence of a face in an image). Image recognition technology is what makes the face detection process possible in the first place.

The Parent Tech: Computer Vision

Computer vision is the term used to describe the category or field of all the technologies, techniques, and processes that enable a machine or computer to interpret, analyze, and make decisions based on visual information.

So, computer vision can be considered the parent, from which image recognition, and many other related processes like object detection, image processing, and so on are derived.

The Talented Child: Image Recognition

Image recognition, simply explained, is the process or ability of a computer to specifically identify objects, features, people, places, and so on in an image or video. This process happens through the use of artificial intelligence, machine learning, and deep learning.

How Does Image Recognition Actually Work?

In today’s day and age, with the advent of powerful artificial intelligence technology that includes machine learning, and deep learning as stated, image recognition uses these processes for much higher accuracy and enhanced recognition.

So, AI models are fed with large amounts of pre-labeled data, to teach them how to recognize what different kinds of images contain. After going through multiple iterations of analyzing the data, the model can recognize data in images to a high level of sophistication and accuracy.

The Step-by-Step

For AI image recognition with machine learning, the first step is of course to provide an image for the system to work on. Then, the image is prepared for analysis through multiple preprocessing techniques. These techniques can include de-noising, brightness and exposure adjustment, and so on.

Then the image identification process starts with segmentation, where the image is divided into segments that would make it easier to analyze. This can include creating thresholds based on pixel intensity values, identifying boundaries through edge detection, and otherwise grouping similar pixels.

Then, the significant features in the image are extracted. These are recognized through finding edges and corners, and things like texture. These extracted features are compared with features from a database. Based on matching, the image is classified by the system independently making decisions based on its analysis, into one of many predefined categories.

Image Recognition vs Object Detection

The terms image recognition and object detection are very similar, causing them to often be confused with each other. But the job of image recognition vs object detection is actually slightly different:

• Image Recognition is more of an image classifier. What this means is that image recognition would look at an image, and identify the overall concept presented. So for an image of a rock concert, an image recognition model would return a single label, like ‘concert’.
• Object Detection on the other hand works to detect, and importantly, localize the significant objects that are inside of an image. So for the same image of the rock concert, an object detection algorithm would detect and label the people, the speakers, guitars, mics, and so on.

The key to understanding the difference here is in the terms ‘detection’ and ‘recognition’. Recognition in this context means just the ability to recognize what is in an image, while detection is the ability to specifically be able to detect each significant object inside of it.

Something to remember is that Image recognition and object recognition are very closely related, and thus often interchangeably used (image here being an all-encompassing term, including things like features and patterns in addition to objects). Image detection and object detection, in a similar way, are often used interchangeably to mean the same thing.

Why does Image Recognition Matter?

As providers of facial recognition and related security solutions, we are obviously biased. But there are actually a multitude of benefits that image or photo recognition can provide in many different industries and applications. Here are a few examples:

• In manufacturing, you can identify, through a camera, parts that might be defective. Compared to manual checking, automation through image recognition allows you to check thousands of parts in seconds.
• In healthcare, an image recognition system can identify possible diseases, illnesses, or kinds of injuries just from looking at medical images. This can help for faster and more efficient doctoral visits, by pre-screening patients and prioritizing more serious illnesses.
• On social media, image recognition is what allows the platforms to automatically detect (and tag) people in photos. So, to some extent, the people-connecting ability of these platforms depends on image recognition.
• In robotics, image recognition is used to identify objects in the robot’s path. This is especially useful in driverless taxis and transport, as they allow the car to recognize stop signs, pedestrians, and other vehicles on a road.

Along with all these benefits, of course, is the ability provided by picture recognition to recognize human faces in images.

What It Does for Facial Recognition

The goal in facial recognition is of course to identify the person in a photo. At this point, it is quite clear how facial recognition depends on AI picture recognition to be able to do this on a technical level. 

But let’s further elaborate on some of the practical benefits face biometrics derives from using AI image recognition:

Unparalleled Speed

The longer a facial recognition system takes to analyze an image, the higher the possibility of a malicious actor manipulating it. Using image recognition allows for real-time facial recognition, which means that your solution is more user-friendly and more secure.

Better Fraud Prevention

Using a powerful image recognition algorithm leads to better fraud prevention by removing or minimizing vulnerabilities for hackers and other malicious individuals to exploit. This means security measures like liveness detection are much less likely to be circumnavigated.

High Scalability

Since modern artificial intelligence-powered systems can easily handle millions of images with ease, it makes them highly suitable for use even in large-scale systems like national ID programs, multinational organizations, and so on.

Facia – The Natural Evolution

Image and face recognition go hand in hand, with the latter being the natural evolution and application of the former in the domain of helping human beings. Our facial recognition and 3D liveness detection solutions here at Facia utilize powerful AI-based image recognition to deliver some of the fastest, most secure, and user-friendly facial identification and authentication systems in the world. Want to make your business enterprise the most protected it could possibly be? Contact our experts today!