Knowledge Сheck: How Well Do You Understand AI Image Recognition?

Opinion pieces about deep learning and image recognition technology and artificial intelligence are published in abundance these days. From explaining the newest app features to debating the ethical concerns of applying face recognition, these articles cover every facet imaginable and are often brimming with buzzwords. You can be excused for finding it hard to keep up with the hype, especially if your business doesn’t routinely intersect with high-tech solutions and you became interested in the capabilities of computer vision only recently.

Do not despair. In this article, you will find a concise but comprehensive overview of what image recognition is, why is it so useful, how people managed to make machines see, what image recognition has achieved so far in various business fields and what the future holds for the technology. Having the basics is important to start asking the right questions and implement meaningful change. Strap in!

Why we need computer vision

Right off the bat, we need to make a distinction between perceiving and understanding the visual world. Various computer vision materials and products are introduced to us through associations with the human eye. It’s an easy connection to make, but it’s an incorrect representation of what computer vision and in particular image recognition are trying to achieve. The brain and its computational capabilities are the real drivers of human vision, and it’s the processing of visual stimuli in the brain that computer vision models are intended to replicate.

How human vision works

Human vision is highly influenced by our expectations and biases. The information we learn through our entire lives determines what details we really pay attention to (e.g. the human eye is hard-wired to prioritize faces in any image, to the point where it sometimes finds facial features in inanimate objects).

This ability of humans to quickly interpret images and put them in context is a power that only the most sophisticated machines started to match or surpass in recent years. Even then, we’re talking about highly specialized computer vision systems. The universality of human vision is still a dream for computer vision enthusiasts, one that may never be achieved.

Why, then, put so many resources into trying to imperfectly imitate human sight?

In reality, only a small fraction of visual tasks require the full gamut of our brains’ abilities. More often, it’s a question of whether an object is present or absent, what class of objects it belongs to, what color it is, is the object still or on the move, etc. Each of these operations can be converted into a series of basic actions, and basic actions is something computers do much faster than humans. When the right technology is used, faster also means cheaper.

Another crucial factor is that humans are not well-suited to perform extremely repetitive tasks for extended periods of time. Occasional errors creep in, affecting product quality or even amplifying the risk of workplace injuries. At the same time, machines don’t get bored and deliver a consistent result as long as they are well-maintained.

And so it is with image recognition — a computer vision approach that helps machines to “understand” images and videos by classifying and labeling them. Recognizing images is a mission-critical step for many computer vision-based solutions, regardless of the industry they are used in. Just to give you an idea of its capabilities, automated image recognition is already being used to perform:

• Customer emotion recognition aimed at boosting in-store experiences
• Public threat detection from CCTV footage
• Compliance with media content regulations
• Brand safety monitoring
• Advanced sports training and post-traumatic rehabilitation
• Exam control via online education platforms
• Financial fraud detection and prevention
• Diagnostics based on medical imaging

How image recognition works with AI

It must be noted that artificial intelligence is not the only technology in use for image recognition. Such approaches as decision tree algorithms, Bayesian classifiers, or support vector machines are also being studied in relation to various image classification tasks. However, artificial neural networks have emerged as the most rapidly developing method of streamlining image pattern recognition and feature extraction. As a result, AI image recognition is now regarded as the most promising and flexible technology in terms of business application.

AI models rely on deep learning to be able to learn from experience, similar to humans with biological neural networks. During training, such a model receives a vast amount of pre-labelled images as input and analyzes each image for distinct features. If the dataset is prepared correctly, the system gradually gains the ability to recognize these same features in other images.

Deep learning workflow for image recognition

The next step is separating images into target classes with various degrees of confidence, a so-called ‘confidence score’. The sensitivity of the model — a minimum threshold of similarity required to put a certain label on the image — can be adjusted depending on how many false positives are found in the output.

One of the biggest challenges in machine learning image recognition is enabling the machine to accurately classify images in unusual states, including tilted, partially obscured, and cropped images. This is a task humans naturally excel in, and AI is currently the best shot software engineers have at replicating this talent at scale.

Now that we learned how deep learning and image recognition work, let’s have a look at two popular applications of AI image recognition in business.

Face recognition

A deep learning model specifically trained on datasets of people’s faces is able to extract significant facial features and build facial maps at lightning speed. By matching these maps to the approved database, the solution is able to tell whether a person is a stranger or familiar to the system.

Facial recognition steps

The known applications of face recognition technology range from something very casual, like tagging pictures on Facebook with the names of the people appearing in the image, to high-performance real-time security systems implemented in banks and airports.

AI-based face recognition opens the door to another coveted technology — emotion recognition. A specific arrangement of facial features helps the system estimate what emotional state the person is in with a high degree of accuracy. Industries that depend heavily on engagement (such as entertainment, education, healthcare, and marketing) keep finding new ways to leverage solutions that let them gather and process this all-important feedback.

Object recognition

Artificial intelligence demonstrates impressive results in object recognition. A far more sophisticated process than simple object detection, object recognition provides a foundation for functionality that would seem impossible a few years ago.

Now, customers can point their smartphone’s camera at a product and an AI-driven app will tell them whether it’s in stock, what sizes are available, and even which stores sell it at the lowest price. A content monitoring solution can recognize objects like guns, cigarettes, or alcohol bottles in the frame and put parental advisory tags on the video for accurate filtering. A self-driving vehicle is able to recognize road signs, road markings, cyclists, pedestrians, animals, and other objects to ensure safe and comfortable driving.

Industries that have been disrupted by AI image recognition

Finance

As digital images gain more and more importance in fintech, ML-based image recognition is starting to penetrate the financial sector as well. Face recognition is becoming a must-have security feature utilized in fintech apps, ATMs, and on-premise by major banks with branches all over the world. It’s reliable, non-intrusive, and fast, making it a hit with customers.

Object recognition is combined with complex post-processing in solutions used for document processing and digitization. Another example is an app for travellers that allows users to identify foreign banknotes and quickly convert the amount on them into any other currency.

Retail

Retail is now catching up with online stores in terms of implementing cutting-edge techs to stimulate sales and boost customer satisfaction. Object recognition solutions enhance inventory management by identifying misplaced and low-stock items on the shelves, checking prices, or helping customers locate the product they are looking for. Face recognition is used to identify VIP clients as they enter the store or, conversely, keep out repeat shoplifters.

Logistics

The logistics sector might not be what your mind immediately goes to when computer vision is brought up. But even this once rigid and traditional industry is not immune to digital transformation. Artificial intelligence image recognition is now implemented to automate warehouse operations, secure the premises, assist long-haul truck drivers, and even visually inspect transportation containers for damage.

Healthcare

Medical images are the fastest-growing data source in the healthcare industry at the moment.
AI image recognition enables healthcare providers to amplify image processing capacity and helps doctors improve the accuracy of diagnostics.

AI rivals radiologists in X-ray diagnostics

Artificial intelligence image recognition is applied to screen patients for different types of cancer, highlight pathogenic blood cells, identify dental implants, reduce attrition rates in rehabilitation programs, and even estimate blood loss during an operation.

Media and entertainment

Face and object recognition solutions help media and entertainment companies manage their content libraries more efficiently by automating entire workflows around content acquisition and organization. Besides, machine learning image recognition is employed to accelerate content retrieval (e.g. when a complex search request is made), streamline ad insertion (by avoiding scene splitting), and improve regulations compliance (by filtering explicit or violent images).

Public safety

Deep image and video analysis have become a permanent fixture in public safety management and police work. AI-enabled image recognition systems give users a huge advantage, as they are able to recognize and track people and objects with precision across hours of footage, or even in real time. Solutions of this kind are optimized to handle shaky, blurry, or otherwise problematic images without compromising recognition accuracy.

Besides generating metadata-rich reports on every piece of content, public safety solutions can harness AI image recognition for features like evidence redaction that is essential in cases where witness protection is required.

What the future holds

Now that you have a concept of what benefits AI brings to image recognition, you might be curious what’s in store for the technology. Does it hold up well against new global challenges?

By all accounts, image recognition models based on artificial intelligence will not lose their position anytime soon. More software companies are pitching in to design innovative solutions that make it possible for businesses to digitize and automate traditionally manual operations. This process is expected to continue with the appearance of novel trends like facial analytics, image recognition for drones, intelligent signage, and smart cards.

There’s no denying that the coronavirus pandemic is also boosting the popularity of AI image recognition solutions. As contactless technologies, face and object recognition help carry out multiple tasks while reducing the risk of contagion for human operators. A range of security system developers are already working on ensuring accurate face recognition even when a person is wearing a mask.