Facial recognition used by security cameras based on Artificial Intelligence is becoming more common, but do you know how this technology works?
Artificial Intelligence (AI) seems to be getting smarter as the years go by, it is implemented in more and more devices and it continues to improve. And, as you already know, the more you train and the larger the database, the more complete and effective it will be.
Well, one of the fields where it works better and better is the detection of faces or objects, also called facial detection, a computer technology based on Artificial Intelligence that is used to find and identify human faces in digital images.
Facial detection technology can be applied to various fields, although security is the most common as it provides surveillance and tracking of people in real time.
In facial analysis, face detection using cameras helps to identify the parts of an image or video where aspects such as age, gender or even emotions need to be focused on from facial expressions, in order to extract data and conclusions.
How does AI face detection work?
For this type of explanation, it is always useful to make comparisons and since we are talking about an Artificial Intelligence, why not compare it with a real one, like the human one. Well, humans recognize images using our neural network that helps us identify objects thanks to the database we have, our brain.
In the same way, the artificial neural network helps machines to recognize images, although that database that we have and that we fill with information based on experience is the part that we must provide to the AI.
Noting that image recognition is more complicated than you might think, as there are several things involved like deep learning, neural networks, and sophisticated image recognition algorithms to make this possible in cameras , for example.
Well, face detection algorithms usually start by looking for human eyes , one of the easiest features to detect. It can then try to detect the eyebrows, mouth, nose, and iris. Once it concludes that it has found a face, it applies additional tests to confirm that it has indeed detected it.
To ensure accuracy, algorithms must be trained on large data sets incorporating hundreds of thousands of images . If we are talking about objects instead of people, visual search technology recognizes the objects in the image and searches the web to see what it is.
Techniques AI uses to identify a face or object
Some of the more specific techniques used in face detection are:
1. Background removal. For example, if an image has a solid, monocolor background or a predefined static background, removing the background can help reveal the boundaries of the face.
2. In color images, sometimes skin color can be used to find faces; however, this may not work with all skins as we already know.
3. Use movement to find faces. In real-time video, a face is almost always moving, so users of this method need to calculate the moving area. One of the drawbacks of this method is the risk of confusion with other objects moving in the background.
4. Combination of the mentioned strategies, which can provide an improvement for face detection.
Convolutional neural network and single shot detector as major revolutions in the field of face detection
Of course, the detection of faces in images can be difficult due to the variability of factors such as pose, expression, position and orientation or even skin color, but some advances have gradually improved this AI.
It should be noted that algorithms such as the region-based convolutional neural network (R-CNN) and the single shot detector (SSD) have helped to improve processes.
A convolutional neural network is a type of artificial neural network used in image recognition and processing that is specifically designed to process pixel data. An R-CNN then generates region proposals from a CNN to solve the problem.
With all this on the table and despite the fact that face identification through cameras attracts attention, AI can help us in a multitude of related fields such as: marketing (showing specific ads when a specific face is recognized), improving the precision of cameras or even applications that can help people with autism to interpret society.
