According to the Center for Disease Control and Prevention (CDC). The factors affecting the spread of the Coronavirus are:

• When a surface becomes contaminated and when a person touches the surface.
  
• The deposition of expelled virus particles onto surfaces, which is affected by airflow and ventilation
  
  

A fingerprint-based authentication system will be prone to touches from multiple people. The constant usage of the reader will reduce the time between when the surface is contaminated and when a fellow user uses it, causing an increase in the rate of spread of the virus.

For this reason, we at CBeyond Technologies are working on a cloud-based face authentication solution to provide apartment complexes and companies that currently rely on a touch-based authentication system. We utilise a state-of-the-art face matching algorithm to figure if the person at a given checkpoint is authorised to enter or not.

The solution will be completely automated, requiring no human intervention for the authorisation process. The only requirement being the user must provide a recent well-lit photograph for the algorithm. The user must make sure their face is observable during the authentication process. Considering current fingerprint solutions must be cleaned thoroughly after each use, our face authentication system will be more straightforward. It will require lower time to authenticate the same amount of people.

Want to learn more about computer vision? Read on…

What is Computer Vision?

Computer vision is one of the fields of artificial intelligence that trains and enables computers to understand the visual world. Computers can use digital images and deep learning models to accurately identify and classify objects and react to them.

How Does Computer Vision Work?

Computer vision is similar to solving a jigsaw puzzle in the real world. Imagine that you have all these jigsaw pieces together and you need to assemble them in order to form a real image. That is exactly how the neural networks inside a computer vision work. Through a series of filtering and actions, computers can put all the parts of the image together and then think on their own. However, the computer is not just given a puzzle of an image - rather, it is often fed with thousands of images that train it to recognize certain objects.
For example, instead of training a computer to look for pointy ears, long tails, paws and whiskers that make up a cat, upload and feed millions of images of cats to the computer. This enables the computer to understand the different features that make up a cat and recognize it instantly.

Computer Vision Applications

Computer vision is being used in more areas than you might expect. From detecting early signs of cancer to enabling automatic checkouts in retail places, computer vision has made its way into our lives. Here are some more computer vision applications:

• Face recognition - Snapchat, Instagram, Facebook and many other social media apps use face-detection algorithms to recognize you in pictures and apply filters on your face.
• Surveillance - Surveillance cameras use computer vision to help detect suspicious behavior in public and private locations.
• Image retrieval - Google Images help you find relevant images when you upload an image. There are different algorithms that analyze the content in the image uploaded and return results based on the best-matched content.
• Biometrics - Fingerprint and iris recognition are some common methods in biometric identification that uses computer vision.
• Smart cars - Smart cars use computer vision to detect traffic signs and lights and other visual features when the cars go on auto mode.

Computer Vision Algorithms

Computer vision algorithms include the different methods used to understand the objects in digital images and extract high-dimensional data from the real world to produce numerical or symbolic information. There are many other computer vision algorithms involved in recognizing things in photographs. Some common ones are:

• Object Classification - What is the main category of the object present in this photograph?
• Object Identification - What is the type of object present in this photograph?
• Object Detection - Where is the object in the photograph?
• Object Segmentation - What pixels belong to the object in the image?
• Object Verification - Is the object in the photograph?
• Object Recognition - What are the objects present in this photograph and where are they located?
• Object Landmark Detection - What are the key points for the object in this photograph?

Computer Vision Benefits

Computer vision can automate several tasks without the need for human intervention. As a result, it provides organizations with a number of benefits:

• Faster and simpler process - Computer vision systems can carry out repetitive and monotonous tasks at a faster rate, which simplifies the work for humans.
• Better products and services - Computer vision systems that have been trained very well will commit zero mistakes. This will result in faster delivery of high-quality products and services.
• Cost-reduction - Companies do not have to spend money on fixing their flawed processes because computer vision will leave no room for faulty products and services.

Computer Vision Disadvantages

There is no technology that is free from flaws, which is true for computer vision systems. Here are a few limitations of computer vision:

• Lack of specialists - Companies need to have a team of highly trained professionals with deep knowledge of the differences between AI vs. Machine Learning vs. Deep Learning technologies to train computer vision systems. There is a need for more specialists that can help shape this future of technology.
• Need for regular monitoring - If a computer vision system faces a technical glitch or breaks down, this can cause immense loss to companies. Hence, companies need to have a dedicated team on board to monitor and evaluate these systems.