The huge potential of facial recognition technology in various fields is almost unimaginable. However, certain common pitfalls in its functionality and some ethical considerations need to be addressed before its most complex applications can be implemented.
An accurate facial recognition system uses biometric technology to draw facial features from photos or videos. It compares this information to a database of known faces to find a match. Facial recognition can help verify a person's identity, but it also raises privacy concerns.
A few decades ago, we could not have predicted that facial recognition would become an almost indispensable part of our lives in the future. From unlocking smartphones to conducting online (or offline) transactions, this technology is deeply ingrained in our daily lives. This is an incredible application of computer vision and machine learning components of artificial intelligence.
Facial recognition systems work like this:
#The trained algorithm determines various unique details of a person’s face, such as the pixels between the eyes numbers or lip arcs, logically interpreted among other details to reconstruct the face within the system. These reconstructed faces are then compared to a large set of faces stored in the system's database. If the algorithm detects that the reproduced face mathematically matches a face present in the database, then the system "recognizes" it and performs the user's task.
In addition to completing the entire process in a few nanoseconds, today's facial recognition systems are capable of working even in low light, poor image resolution and viewing angles.
Like other artificial intelligence technologies, facial recognition systems need to follow some ethical principles when used for various purposes. These regulations include:
First of all, the development of face recognition equipment must make the system completely prevent, or at least minimize Reduce bias against any person or group based on race, gender, facial features, deformities, or other aspects. There is now ample evidence that facial recognition systems are unlikely to be 100% fair in their operation. As a result, companies building systems to support this technology often spend hundreds of hours removing all traces of bias found in their systems.
Reputable organizations like Microsoft often hire qualified experts from as many ethnic communities as possible. During the research, development, testing and design phases of their facial recognition system, diversity allowed them to create massive data sets to train AI data models. While large data sets reduce the bias quotient, diversity is also symbolic. Selecting individuals from around the world helps reflect the diversity of the real world.
To eliminate bias from facial recognition systems, organizations must make extra efforts. To achieve this, the datasets used for machine learning and labeling must be diverse. Most importantly, the output quality of a fair facial recognition system will be incredibly high, as it will work seamlessly anywhere in the world, without any element of bias.
To ensure the fairness of the facial recognition system, developers can also involve end customers during the beta testing phase. The ability to test such a system in real-world scenarios will only improve the quality of its functionality.
Organizations using facial recognition systems in workplace and cybersecurity systems need to know where machine learning information is stored all details. Such organizations need to understand the limitations and capabilities of the technology before implementing it in daily operations. Companies providing AI technology must be fully transparent with customers about these details. Additionally, service providers must ensure that their facial recognition systems can be used by customers from any location, based on their convenience. Any updates in the system must be validly approved by the client before proceeding.
As mentioned above, facial recognition systems are deployed in multiple departments. Organizations that manufacture such systems must be held accountable for them, especially where the technology may directly impact any person or group (law enforcement, surveillance). Accountability in such a system means including use cases to prevent physical or health-based harm, financial misappropriation, or other problems that may arise from the system. To introduce an element of control into the process, a qualified individual takes charge of the systems in the organization to make measured and logical decisions. Beyond this, organizations that incorporate facial recognition systems into their daily operations must immediately address customer dissatisfaction with the technology.
Under normal circumstances, the face recognition system shall not be used to spy on individuals, groups or other behaviors without the consent of the individual or group. Some institutions, such as the European Union (EU), have a standardized set of laws (GDPR) to prevent unauthorized organizations from spying on individuals within the jurisdiction of the governing body. Organizations with such systems must comply with all U.S. data protection and privacy laws.
Unless authorized by the national government or decisive governing body, used in connection with national security or other high-profile situations purpose, otherwise an organization cannot use a facial recognition system to monitor any person or group. Basically, this technology is strictly prohibited from being used to violate the human rights and freedoms of victims.
Although facial recognition systems are programmed to comply with these regulations without exception, they can cause problems due to operator errors. Some of the major issues related to this technology are:
As mentioned earlier, facial recognition is included in digital payment apps system through which users can verify transactions. Due to the existence of this technology, criminal activities for payment purposes such as facial identity theft and debit card fraud are very possible. Customers choose facial recognition systems because they provide users with great convenience. However, a mistake can occur in such a system when identical twins use them to make unauthorized payments from each other's bank accounts. The concern is that despite the security protocols in place in facial recognition systems, face copying can lead to the misappropriation of funds.
Facial recognition systems are used to identify public criminals before they are captured. While the technology as a concept is undoubtedly useful in law enforcement, there are some obvious problems with its working. Criminals can abuse this technology in several ways. For example, the concept of biased AI provides inaccurate results for law enforcement officers because the systems sometimes fail to distinguish between people of color. Typically, such systems are trained on datasets containing images of white men. So the way the system works is wrong when it comes to identifying people from other races.
There have been several cases of organizations or public institutions being accused of using advanced facial recognition systems to illegally spy on civilians. Video data collected by individuals under constant surveillance can be used for a variety of nefarious purposes. One of the biggest drawbacks of facial recognition systems is that the output they provide is too general. For example, if a person is suspected of committing a felony, their picture will be taken and run along with pictures of several criminals to check if the person has any criminal record. However, stacking this data together means that the facial recognition database will retain photos of the man and experienced felons. So, even though the individual is relatively innocent, his or her privacy is violated. Second, the person may be viewed in a bad light despite being innocent by all accounts.
As mentioned, the main problems and errors associated with facial recognition technology stem from a lack of advancement in the technology, a lack of diversity in the data sets, and inefficient handling of the system by organizations. However, the scope of application of artificial intelligence and its applications in real needs should be unlimited. Risks with facial recognition technology often occur when the technology does not work in the same way as is actually required.
But it is foreseeable that with the continuous advancement of technology in the future, technology-related problems will be solved. Issues related to bias in AI algorithms will eventually be eliminated. However, for this technology to work flawlessly without violating any ethical norms, organizations must maintain a strict level of governance for such systems. With greater governance, facial recognition system bugs could be addressed in the future. Therefore, the research, development, and design of such systems must be improved to achieve positive solutions.
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