Top 7 Facial Recognition Technologies

In recent years, biometric tools like facial recognition technologies have witnessed some groundbreaking innovations. From unlocking your mobile phone to automatically tagging photographs to diagnosing patients with genetic conditions, the possibilities are now endless.

The research firm Markets and Market expects the global facial recognition market to be worth $8.5 billion by 2025. However, the success of all these face recognition tools depends largely on adequate training data. In this scenario, the more extensive and more inclusive the data is, the better.

But before we get ahead of ourselves, let’s first define it.

Applications of Deep Learning for Computer Vision

Computer vision technology powered by Deep Learning (DL) provides real-world value across industries. Such intelligent technologies have been around for a few years, and it’s finally coming of age and rising in prominence.

In fact, computer vision is precisely what makes driverless cars possible. However, there’s a myriad of possibilities and use cases, including the augmentation of human sight.

Artificial intelligence for efficient support in translation work

Artificial Intelligence (AI) is becoming an ever more important part of our lives. Whether it is in our homes with smart speakers and automation or in the business world, its impact in our lives cannot be dismissed.

However, while the benefits of AI are obvious, in the past, using the technology with language translation was difficult, if not impossible. Language translation is an area that has always required human intervention. There’s simply too much nuance in language for a machine to understand without a lot of training, most often done painstakingly by hand.

In recent years, that situation has started to change. With new advances in Machine Learning (ML) along with the development of neural networks, this once-difficult task is now much more possible.

Object Detection and Segmentation

In recent years, object detection and segmentation have accelerated significantly. Today, smart algorithms can find and classify countless individual objects within a video or an image. Although it was incredibly difficult for machines to do, it’s now part of our daily existence.

Both object detection and segmentation are powered by Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). In this scenario, convolutional neural networks can locate and identify the class each item belongs to within an image.

It has also evolved to be much more than an intelligent algorithm that can recognize objects in photographs stored in a database. It can now find and classify objects in real-time to enable things like self-driving autonomous vehicles and more.

The Process of AI Training

When most people think about artificial intelligence (AI), they think of two possible futures. A positive future where self-driving cars help us navigate our roads and robot servants help us maintain our homes. Or a more negative one, where machines take away our jobs and employment.

Fortunately, it looks like the negative future isn’t one that we have to worry about. AI systems won’t replace humans in the workforce, but rather they’ll exist alongside humans as invaluable sidekicks. While self-driving cars are well on their way to reality, some of our other grandiose goals for AI are still waiting for fruition, however, before we get there, more work needs to be done.