Technology, AI, and facial recognition have all been making advancements in the past few years, leading to a more streamlined pathway that has led to easier and more efficient ways to identify, authenticate, and verify individuals using their facial features. With these advancements in technology, AI, and facial recognition, businesses have begun to use them in more ways than ever before.
Facial recognition technology has been utilized for a number of reasons in numerous sectors, including security, customer service, and marketing. As this technology advances, it is critical to grasp the technical aspects of its application. To use facial recognition technology, the user must be in front of a digital system (such as a camera) that can access the user’s facial data or image. After that, the system compares the image to a database of known face traits and matches it to a specific individual. In customer service applications, such as customer check-in systems, facial recognition can be used to quickly identify customers and provide personalized service.
Further, the integration of artificial intelligence and face recognition patterns has paved the road for businesses to rapidly and effectively identify and verify customers, workers, and transactions. Businesses have been able to eliminate fraud, increase customer service and assistance, and make better decisions as a result of this technology. As technology advances, the potential for these technologies to further simplify and improve company operations increases.
Historically, the early systems of FR relied on manual entry of facial features, such as eyes, nose, and mouth, and were not very accurate because of the absence of neural network topologies. In the 1990s, with the advent of digital cameras and more accurate configuration of faces, the accuracy of FR improved due to the development of algorithms that could accurately recognize and compare facial features.
In the early 2000s, Carnegie Mellon University researchers created a facial recognition system that could recognize human faces in digital photographs. As the first commercial face recognition system to be applied in a real-world setting, this opened up new technological possibilities. In the same timeline, with the advanced spoofing and data manipulation issues come across, developers began to utilize sophisticated algorithms to determine if a person was actually present in front of the camera and that set a pathway to Facial liveness systems.
Although the field of FR has existed for long, recent advances in the development of Machine learning (and deep learning) aided by AI have made it more applicable. For instance, FR and FL are now a crucial part of KYC and a more secure access management system. A robust FR technology, supplemented with FL, can now blacklist, de-duplicate, authenticate and catch data manipulation if implemented smartly.
How FR could act as a layer in security protocols, let’s find out.
When the data manipulation was arising by using duplicate images and HighRes videos/images to trick the FR system, the concept of liveness detection of the face has driven the researchers’ minds. In a case study of Jukshio and Jio’s attendance management system, facial liveness enhanced their attendance-based issues using a deep-learning version of FL detection algorithms. It paved the way for the realization that FR with FL is the new beginning in biometrics and AI/ML modules.
The foundation of facial recognition technology is an artificial neural network, which abstracts the human brain’s neuron network from the standpoint of information processing, produces a straightforward model, and creates various networks using various techniques of connection. An operational model called a neural network is made up of several nodes (also known as neurons) that are interconnected. An activation function, a particular output function, is represented by each node. An artificial neural network’s memory is represented by the link between every pair of nodes as a weighting value for transmitting the connection signal.
Apart from a robust facial recognition system, a crucial neural network to produce secure AI layers, Facial liveness detection is also a security measure that helps to confirm that a digitally captured image or video frame is of a real person. It works by analyzing facial movements and expressions to determine whether the person is real or not. Many financial institutions are now using facial liveness detection in their customer authentication processes.
The growing demand for air travel has resulted in airport overcrowding, resulting in delays, cancellations, and greater stress for passengers. To address the issue of unprecedented airport congestion globally, governments and organizations have developed a variety of digital solutions to alleviate the strain of airport congestion.
DigiYatra, an effort of the Indian government, is one such option. This program intends to simplify air travel by using a digital ID system. Travelers will be able to utilize their ID cards or cell phones to access services such as check-in, security, and boarding using this system. The system also uses face recognition technology to automate the identification verification procedure, thus reducing waiting times.
Other institutions, in addition to DigiYatra, are implementing facial recognition (FR) technology in various ways. The International Air Transport Association (IATA) has put in place a mechanism that uses FR to expedite boarding.
This technique allows passengers to be identified by scanning their mobile device or boarding ticket with their faces. The need for manual identification verification is eliminated, resulting in a speedier and more secure boarding process.
Other institutions are also putting FR to use in novel and creative ways. Airports in the United States, for example, have begun to employ FR to track and identify persons who may represent a security danger. These technologies examine face traits and compare them to a database of known dangers using sophisticated algorithms.
In the era of information warfare where data is the new fuel, its security and proper assimilation into databases are posing an immense load on developers creating algorithms and codes, data mining engineers, and big data researchers, who are trying every minute to enhance AI-led FR to act in a more robust and agile manner. More developments are required to mitigate FR/FL shortcomings such as phishing-proof AI models, accuracy in identifying a face in low-light conditions, etc., and organizations, including Juskhio, are flexible in adopting even a minute change in security and accuracy.
