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Used for everything from unlocking mobile phones and personalising ads to tracking criminals, let’s get under the hood to understand what makes face recognition tech tick.
A technology solution where algorithms are used to analyse and identify the faces of individuals found in video footage or digital images.
In the sixties, development had already begun on similar systems in the form of computer applications. Today, the level of sophistication in the technology enables detection, matching and recognition of a human face against a database of images of faces.
There are three main stages of the facial recognition process: detection, matching and recognition.
Under ideal conditions it can be highly accurate, but this can be influenced by factors such as lighting, angle of the face and image or video quality. Facial recognition tech can also be impacted by biases in algorithms or within the datasets used to train the technology, which can result in false positives or false negatives.
Data from a recent evaluation by the U.S Department of Commerce shows that “each of the top 150 algorithms are over 99% accurate across black male, white male, black female and white female demographics.”
Benefits continue to increase with the developments in facial recognition systems. The current offerings support the following:
The identification, authentication, surveillance and forensics processes are all bolstered by facial recognition systems, enabling security professionals to quickly identify individuals entering or exiting secure areas or events, grant access to systems or locations, monitor and track people in real-time and identify suspects in criminal investigations.
Algorithms are able to consistently analyse and compare facial features and do so objectively without human bias or subjective judgement. These facts eliminate the risk of human error and increase accuracy of results.
Facial recognition technology can be easily integrated with existing security systems, such as cameras, access control systems or video surveillance systems, without requiring major hardware upgrades. It’s also quite flexible technology in that it can be customised to suit different applications, such as authentication for mobile devices, access control for buildings or public space surveillance.
The identification and authentication processes can be automated with facial recognition systems, reducing the need for manual intervention. This makes it easier to manage large datasets of facial images, saving on resources.
Some of the key settings that the technology is used in include fraud detection, cyber security, airport border control and banking.
Increasingly used in fraud detection, facial recognition is helpful in verifying the identity of individuals making high-value transactions or opening accounts and can be used to match faces against watchlists of known fraudsters or suspects. Biometric authentication can also be used to prevent unauthorised access to accounts or systems.
Facial recognition systems provide an additional layer of security when it comes to cyber environments. Multi-factor and biometric authentication can both be enhanced using the technology.
The speed and efficiency of passenger processing is increasingly supported by facial recognition technology, while also enhancing security. Some airports use the tech for automated passport control, allowing passengers to scan their passport and face at self-serve counters, and the systems are also used to track passengers throughout the airport to help improve security. Identity verification is also vital in identifying each passenger entering or exiting a country, reducing the risk of identity fraud.
Customer experience is streamlined, and security increased with facial recognition. It can be used to authenticate customer identity when they log into accounts and make transactions, while some big banks are exploring the use of the technology for ATM access, in place of using a card or PIN number.
We’ve rounded up some of the top software for you to explore.
Developed by Amazon Web Services (AWS), this software uses deep learning algorithms to recognise, analyse and compare faces in images and video.
This software company creates a range of solutions for biometric face recognition. Cognitec’s software uses advanced algorithms that are used for a variety of applications including security, surveillance, ID verification and access control and can detect if someone is wearing a mask. Their marketing states they are committed to ethical and responsible use of the tech and have implemented regular auditing and compliance with data protection guidelines.
With a strong focus on biometric authentication and security, features such as liveness detection assist with knowing if a face is real or not and the algorithms are designed to achieve high accuracy rates, even under challenging conditions such as poor lighting and varied camera angles. BioID is a solid option for businesses that require a high level of security for their systems and applications.
FaceFirst has a strong focus on accuracy, speed and scalability. The algorithms achieve high accuracy rates and the real-time matching enables immediate alerts and notifications. Easily scaled up to handle large volumes of facial data, it’s well suited for use in large-scale environments and enterprise-level applications.
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