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Face Recognition Technology: How it Works

Face Recognition Technology: How it Works

Face recognition technology is the most deployed biometric modality alongside fingerprint and iris recognition. This technology’s ability to detect one face among millions in just a few seconds has led to its widespread adoption in the field of physical security. So far, it has been extremely useful in crime detection and investigation at all levels — from petty crimes like shoplifting to nabbing international criminals and fugitives.

Although facial recognition is relatively new for businesses, INTERPOL and the FBI have been using it for almost a decade. In 2016 alone, INTERPOL used facial recognition technology to identify over 1,000 criminals, fugitives, and persons of interest. Now, it is not just INTERPOL but also the FBI that has been using this technology for quite some time. In fact, by the year 2016, the FBI had profiled more than half of American adults and recorded them in a facial recognition database.

Leveraging these databases, AI video analytics modules have been developed to overcome security concerns such as shoplifting, weapon detection, theft, and other dangerous behavior. These diverse applications have made the facial recognition technology a sought-after biometric modality by the banks, retailers, airports, and other businesses.

However, to get the most out of this technology, it is essential that you pick the right solution. Therefore, we decided to discuss everything you need to know about face recognition technology, how it works, and what impacts its performance.

Market Size of the Face Recognition Industry

Experts estimate that by 2022, the Face Recognition sector would generate $9.6 billion dollars in revenues as it has been registering an annual CAGR of 21.3% per annum since 2016. The facial recognition sector is divided by experts into five segments, such as 2D face recognition, 3D face recognition, thermal recognition, face recognition software, and AI video analytics.

The fast-paced growth can be largely attributed to the increasing application of the technology in various verticals such as airports, large enterprises, governments, and law enforcement agencies. Advances in processing speed, AI video analytics, and deep learning technologies have also made face recognition more practical and accessible.

How does Facial Recognition Technology Work?

Face recognition technologies serve various purposes such as threat detection, criminal investigation, smart advertising, and authentication, among others. These diverse applications bring along a lot of confusion but that vanishes once you understand how it works. Before we discuss that, let us understand the environment required to deploy this technology at your business premises.

Face Recognition Environment

Each organisation has a different situation with its business needs, camera layouts and physical environment. Choosing the right solution will maximise the accuracy. Some factors that must be taken into account are the illumination conditions, climatic conditions, distance between the camera and the subject, specific angles that can be covered, and response time.

A point to note is that some solutions can only capture images at a certain distance and from specific angles and may not be a good fit for a particular location. For instance, one that does not capture distant images would be useless to monitor large campuses such as educational institutions and airports. Likewise, face recognition modules that require bright illumination conditions may not serve the purpose at large warehouses, parking lots, or other places that require 24x7 surveillance.

Scylla’s face recognition technology can function in an unconstrained environment. All it needs is one image of any individual and can accurately detect the target from extreme distances and angles. Moreover, it can work with your existing camera infrastructure. The solution integrates with your camera network through a connector application. This could either be your existing camera infrastructure or you may need to install additional hardware.

How face recognition works

Face Recognition Mechanism

Face recognition works in three steps — facial data collection, data quantification, and data processing.

Facial Data Collection

The facial recognition technology collects facial images through the camera infrastructure. With the advances in data processing speed, facial data can now be collected from real-time images and videos sourced through connected cameras. This technology can capture thousands of faces in just a few seconds.

Data Quantification

The facial data sourced from cameras is then processed through a computer vision algorithm that quantifies it and creates a unique facial signature for every individual. This digital fingerprint, or a ‘feature vector’, is then used for identification. It’s worth noting that this process is irreversible - in most of the cases it is not possible to ideally recreate the image of the face using a feature vector derived from it.

One can use a different number of unique elements in a feature vector. Conventionally accepted minimum number is 80. The way that these measurements are used and converted into a facial signature has a major impact on accuracy.

Data Processing

Once the facial signature is generated by an algorithm, it is then compared to a data set to identify the individual. That data set could be anything from a criminal record set to an employee roster.

The technologies used to process the data determine the response time. Therefore, Scylla works on edge devices from the Nvidia Jetson family (Nano, Xavier, etc.) that speed up the data flow for seamless real-time video surveillance. There is no tradeoff between speed and accuracy. Accuracy levels are as high as 99.85%.

Final Takeaway

Face recognition technology is a breakthrough in video surveillance; it uses unique physiological traits that are visible to the naked eye and the cameras. This allows tracking and tracing the subject without the active involvement of the subject unlike with other biometric verification forms such as fingerprint and palm print.

Advances in deep learning and the need for contactless monitoring of large spaces for criminals and terrorists have fueled the growth of face recognition. Despite these advantages, face recognition did not achieve its fullest potential due to the lack of high-speed data processing technologies. That has changed and with edge computing enabling real-time processing. In conclusion, face recognition will continue to transform the world of physical security and keep people safe in an increasingly risky world.

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