AI-Powered Facial Recognition- Enhanced Technology to Prevent Fraud

AI-Powered Facial Recognition: Enhanced Technology to Prevent Fraud
Source: Pixabay.com

When you open Facebook to post your picture, and the site automatically tags the individuals present in the photo, there is a high chance that you do not give much thought to the technology in work behind it. However, once you realize that facial recognition technology has the ability to track down criminals as they walk down a random road in your area, it might pique arouse your interest and make you curious about this convenient innovation.

Similar to other new technologies that have their pros and cons, the positives of facial recognition most definitely outweigh its negative counterparts. It is essential to be aware of the application and impact of facial recognition that has taken the world by storm.

What is Facial Recognition?

Facial recognition is a biometric verification system that utilizes distinct facial features of a person to accurately identify them. The facial recognition technology market is forecasted to grow by a CAGR of 21.3% during the projected period of 2016 to 2022 and with the global market size expected to reach a value of USD 9.6 billion by 2022.

Today, this technology is used to make our day-to-day lives convenient, be it from unlocking your smartphone, going through airport security checks, buying goods at retail stores, or even shopping online. Numerous industries have made use of this technology for different reasons. However, at the forefront of the pack is fraud prevention. To give a simple real-world example, a famous musician Taylor Swift was able to track and identify her stalker at a Rose Bowl concert in May 2018, all thanks to facial recognition technology and its verification capabilities. 

In today’s technologically advanced world, we are flooded with a mass amount of data, and the overabundance of images and videos available online has provided sufficient datasets to make face detection systems work. Facial recognition analyzes this visual data accumulated through high-quality CCTV (Closed-Circuit Television) cameras that are installed all over the cities for enhancing security systems, mobile devices, social networking sites, and to enable other digital activity. 

With the help of machine learning and artificial intelligence, facial features can be mathematically analyzed for identifying patterns and making comparisons between different images and videos. Once a biometric database has been formed, this technology can be used for accurately identifying identities. Let’s take a deeper look into how this is done. 

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How Does it Work? 

Beyond its use of unlocking smartphones, facial recognition systems work by cross-matching distinct faces of individuals as they walk past CCTV cameras, to pictures of individuals present on a watch list. These watch lists can include images of anyone, including those that are not suspected of any wrongdoing, and the images can be obtained from anywhere — even from our private social networking accounts. Here’s how this technology works:

  • Step 1: Face Detection

A CCTV camera identifies and locates the image of an individual, whether they are alone or present in a crowd. 

  • Step 2: Face Analysis

A facial recognition software reads the geometry of a face to verify individuals based on their facial features, such as the distance between the eyes, the width of the forehead, the shape of the chin and ears, the depth of the eye socket, or the contour of the lips. The intention is to accurately identify the facial symmetry landmarks since they are unique to each individual. 

  • Step 3: Conversion – Image into Data

Once an individual’s face has been captured and analyzed, it is transformed into a digital format to make it machine-readable. AI-powered facial recognition uses advanced algorithms to form a unique “faceprint” in the same way thumbprints are created. 

  • Step 4: Finding a Match

After a faceprint has been formed, it is compared against global databases. For instance, the FBI has a dataset of approximately 650 million photos that can be used for detecting and tracking wanted criminals. 

Of all the biometric technologies available today, facial recognition has the highest number of use cases. As mentioned earlier, the top application of this technology is fraud prevention, which is why it is regularly used by law enforcement agencies. 

Related: How Artificial Intelligence Vastly Improves Business Automation

Law Enforcement and Facial Recognition

The law enforcement industry frequently uses facial recognition in routine patrolling and policing. Police officers can obtain mugshots from criminals and match them against global face recognition databases. 

By analyzing mugshot databases, law enforcement officers can identify and locate individuals using pictures found on social media sites or CCTV and traffic cameras. In this way, suspects of illegal activity can be traced before any damage has been done to society. 

Face recognition software is easy to use and can also be integrated into mobile phones. This allows police officers to use smartphones or other portable devices to capture the image of a driver that may be overspeeding, or a pedestrian that may be littering a road. Once an image has been obtained, individuals can be matched against criminal watchlists, Politically Exposed Persons (PEP) lists and global sanctions. 

In order to benefit from this technology, numerous other industries have also been using facial recognition to enhance security systems at airports, border checks, shopping mall entrances, or massive public events such as the Olympic Games. 

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