Introduction
Firstly, Facial recognition is an area of biometrics where different technologies converge. The next generation of mobile networks, 5G, and edge technologies would make it even more widespread. An area where 5G and IoT meet is in the deployment of high-density networks of security cameras back by artificial intelligence. Specifically, the use of high-density networks of cameras that are back by AI. These are use to secure critical infrastructure and smart cities.
So, Few technologies have sparked our imagination like facial recognition. Being the most natural of all biometric measurements, it is one of the most sophisticated surveillance tools in existence. It makes a lot of sense. We recognize ourselves by looking at our faces. Facial recognition software maps, analyzes, and confirms the identity of an individual.
The way we interact with facial recognition devices, especially in public places would have a greater impact on our lives. When the device is yours, you can opt-out or turn off the facial recognition feature. But when facial recognition is in cameras all around in public it is difficult to opt-out. There are questions in society today about how facial recognition should be regulate and its impact on the privacy of individuals.
History of Facial Recognition
Meanwhile, In the 1960s, Wilson Bledsoe developed a system that classified photographs of people’s faces. Using the system, an unknown face could be confirm against the data points of previous photos. At the time, the system was not fast, but it performed the function. In 2001, law enforcement officials used facial expression in the Super Bowl. This was about the first public use of the technology and it drew a lot of controversy. Critics described it as a violation of the 4th Amendment rights against unreasonable search.
How Does Facial Recognition Software work?
For most people, the first contact with facial recognition is in the movies. But it is hardly properly represente. Every facial recognition software works differently. However, the process can be broadly divide into three parts:
Detection– The process of locating a face in an image. Face detection is nothing dangerous. It detects the face in the focus of the camera. If you have ever used a camera with facial detection, it draws a shape around the face to auto-focus.
Analysis– This step maps faces by measuring the distance between the eyes, shape of the chin, the distance between the mouth and nose, the shape of the nose. It converts the information into a string of numbers.
Recognition– An attempt to confirm the identity of a person in a photograph. This process is also use in security features to determine the identity.
The detection stage begins with an algorithm that learns what a face is. Usually, the algorithm is traine initially with photos. Once software is trained to detect faces, it can then find and compare them with other faces in a database. It then displays the results and ranks them according to accuracy. This system is common by law enforcement agents to identify criminals.
COVID Pandemic, A Driver
Investments in facial recognition is increasing and the technologies keep maturing. We see new use cases and the COVID pandemic is an essential driver. This leads to facial recognition systems being combined with other facial recognition technologies. For instance, in enforcing quarantine rules and with respect to contact tracing, facial recognition and other biometrics technologies are used. Presently, there are powerful 3D facial recognition software that overcome the pitfalls of 2D facial recognition and work well in low visibility and complete darkness.
Facial recognition was first used in personal devices as a security feature with Windows Hello and Android Trust Face in 2015. iPhone incorporated the technology into their devices in 2017. Also in 2017, President Trump issued an executive order to use facial recognition at the US borders. Around this period, US stores started to experiment with the technology to identify legitimate shoppers and shoplifters.
Facial Recognition and algorithms
A common feature to all disruptive technologies is the artificial intelligence area precisely deep learning where the system can learn from data. This is important because it is a central component of next-generation algorithms. It holds the keys to face detection and translation of communication in real-time.
In an environment like China, facial recognition has become a hot topic. It is being discussed in different parts of the industry from banks to airports to law enforcement. Authorities in China are expanding facial recognition sunglasses as people are starting to use them in their border towns. China is also setting up a video surveillance network across the country. It has high ambitions in AI and facial recognition technology. It aims to be a world leader in AI by 2030.
Can Facial Recognition Be Fooled?
In Russia, an individual by the name of Grigory Bakunov invented a solution to escape proper face detection and confuse face detection devices. He developed an algorithm that creates special makeup to fool the software. He has chosen not to bring the product to market because it is easy to use for malicious use. In a similar development, a German artist has come up with a device known as CV Dazzle. He has a camouflage that includes patterns in fabric to fool the recognition system.
Conclusion
Finally, Facial recognition is a biometric technology that identifies individuals by matching facial characteristics. The future of facial recognition is about merging technology with applications that involve 5G and IoT.
It captures, analyses and compares patterns based on a person’s facial details. The detection process is essential in detecting and locating human faces in images and video. The capture process transforms analog information into a set of digital information based on the person’s facial features.
