Facial Recognition technology detects faces in the camera’s field of view and matches them against faces previously stored in a database. Anti-spoofing is provided through liveness testing without the need for a stereo or a 3D camera. Face Recognition technology is now taking a further step as it is being combined with IP surveillance.
Gemalto, a part of the Thales Group and a company that focuses on Digital Identification and Data Protection in order to counter the two root causes of cyberattacks, identity theft, and unencrypted data, defines Facial Recognition as the process of identifying or verifying the identity of a person using their face. It is a technology that captures, analyzes, and compares patterns based on the person’s facial details. The face detection process is a basic and essential step allowing the systems to detect and locate human faces in a set of images and videos.
The face capture process transforms the analog information contained by a face into a set of digital information based on the person’s unique facial features. By using this data, the face match process verifies if two faces belong to the same person. Face Recognition is considered to be the most natural of all biometric measurements currently in use today.
Facial Recognition is set to be a major topic for the 2020 Summer Olympic and Paralympic Games in Tokyo, Japan, which are taking place from July 22 to August 9 and from August 25 to September 6 respectively, as the first time the technology is being used in the Olympics. Facial Recognition technology will be used in Tokyo 2020 to identify authorized persons and grant them access automatically, enhancing their experience and safety.
The technology is clearly having a momentum. According to BBC News, U.S. supermarket giant Walmart has confirmed it uses image recognition cameras at check-outs to detect theft. Having used the technology already in more than 1,000 stores, the company has said that it had made “an investment to ensure the safety of our customers and associates.”
Although it could be argued that an ideal environment, such as an airport check-in, where the face is straight on and well lit, and the camera is high-quality, AI-powered face recognition is said to have become now better than human; and this has been this way since at least 2014.
Rise of Facial Recognition for IP surveillance
As advances and innovation around Facial Recognition technology continues to evolve even more, one of the latest trends come from CyberLink’s FaceMe® AI Facial Recognition engine integrated into Vivotek‘s IP surveillance solutions of network cameras and back-end video management software. This integration enable security operators to receive accurate Facial Recognition alerts based on both blacklists and whitelists.
According to Dr. Jau Huang, CyberLink’s Founder and CEO, “the demand for Facial Recognition is booming, driven by the latest IoT and AIoT innovations, and are enabling a wide array of scenarios across industries such as security, home, public safety, retail, banking, and more.” He says that each application is dependent on the performance of the cameras used to capture faces and by integrating FaceMe into Vivotek’s surveillance devices it is possible to bring accurate and reliable new solutions into the market.
Powered by Deep Learning and Neural Network algorithms, Cyberlink FaceMe is one of the most accurate AI Facial Recognition engines, according to the company. However, this statement is totally backed up by a recent Face Recognition Vendor Test (FRVT) conducted by the U.S. National Institite of Standards and Technology NIST, CyberLink ranked 12th among all participants in FRVT 1:1 (WILD 1E-4), confirming that FaceMe is a world-leading facial recognition engine. The NIST report (PDF) details recognition accuracy for 127 algorithms and associates performance with participant names.
The NIST FRVT WILD 1E-4 dataset consists of faces extracted from surveillance camera footage or photos, encompassing a wide array of real-world situations including a range of capture angles, poor lighting, or partially covered faces. The image variability simulates real-world use cases where systems would be required to accurately identify individuals in multiple different settings.
Some applications for this technology include the retail industry, banking, organizations transitioning into digital transformation, and those companies that want to make their offices smarter.
AI facial recognition for smart retail
According to CyberLink, a pioneer of Artificial Intelligence and Facial Recognition technologies, with the FaceMe AI Facial Recognition solution retailers can analyze customer information such as gender, VIP status, emotion, age, and name. An interactive dashboard records real-time in-store analytics. The results can help determine consumer behavior at every touchpoint. A real-time graph shows unique visitors, numbers of visits, average visit time, and total visit time.
AI facial recognition for smart banking
Facial recognition is changing the future of banking by improving security and customer service. According to CyberLink, by detecting spoofing in photos or videos using 3D and 2D anti-spoofing technology it is possible to recognize only real physical humans, offering more security to mobile banking.
AI facial recognition for the smart office
Adopting Facial Recognition based on door security access systems makes it possible for enterprises to track employees and visitors in smart offices, making the office space safer and less vulnerable to intruders or attacks.
Facial Recognition Market worth $7.0 billion by 2024
According to Markets and Markets, retail and eCommerce vertical is going to be the fastest-growing vertical during a report’s forecast period that estimates that the Facial Recognition market will be worth $7.0 billion by 2024.
According to the Markets and Markets report, face recognition helps retailers proactively prevent organized retail crimes. The Facial Recognition technology-based system can instantly alert retail security personnel the moment someone enters a store resembling a documented retail criminal. Facial Recognition also keeps stores safer. The technology empowers security by preventing crimes before they occur.
In addition, Facial Recognition improves retail customer experiences by quickly recognizing VIP customers who opt-in. Retailers can also send tailored text messages to customers in stores that offer special personalized recommendations, discounts, and other offers. Hence, Facial Recognition is gaining traction in the retail and eCommerce vertical rapidly. The technology also seems to be well accepted by most customers, especially those in the younger generations who grew up surrounded by digital devices and the evolving new technologies.
Human facial recognition and the Fusiform Face Area (FFA) in the human brain
Technology once more has merged with science when it took inspiration for developing the Facial Recognition technology we currently use in devices and security cameras. After years of research, the inspiration came from a dedicated part of the human brain responsible for such function: The Fusiform Face Area (FFA).
Researchers from the Department of Brain and Cognitive Sciences at the Massachusetts Institute of Technology (MIT) reported (PDF) in one of their studies that “numerous behavioral and physiological studies have provided evidence that the brain contains special-purpose mechanisms that are selectively involved in the perception of faces. Recent evidence from neuroimaging in humans has demonstrated a region in the fusiform gyrus called the Fusiform Face Area, or FFA, which responds both strongly and selectively to faces.”
Most recently, other researchers have taken a special interest in this part of the brain which responds much more strongly to a wide variety of face stimuli.
In the introduction to their research work, Anthony C. Little, Ph.D at the University of Bath, England, et al write that “faces come in a remarkable range of shapes and sizes and are covered with an incredible number of muscles, adding to facial complexity. Moreover, the importance of faces in human life is highlighted by a great deal of empirical research. Human infants only minutes old attend particularly to face-like stimuli relative to equally complicated non-face stimuli. We rely on faces to recognize the myriad of individuals we encounter in our lives and, consequently, thieves, bank robbers, and superheroes wear masks to conceal their identities. Our faces also display our feelings about past, current, and future events through emotional expressions.”
Face Recognition technology has come a long way to become better than human. And this is only the beginning.