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Facial recognition systems are now more familiar than ever. Regardless of its quick growth, the technology yet remains a black box for the majority of its users. Here, we will be reviewing all the things you need to know about facial recognition systems.
While facial recognition technologies have been in existence of some sort even before the dawn of the century, considerable progress has only been made emphatically in the last decade. Twenty years in, and now we have systems and machines so sophisticated they record more recognition abilities than humans. How does facial recognition work? How does it achieve these?
Facial recognition is any process that involves the use of biometric data (or similar processes), comparison of patterns, mapping, etc. to identify and verify people by their faces. This is to say, facial recognition leverages the distinct features present in human faces to make identifications, verifications, and authorizations. The facial recognition industry has continued to grow steadily and is expected to be worth at least 15 Billion USD by 2025.
Facial recognition correlates patterns deduced from distinct facial contours to identify its test faces. How has it become so good? How is unlocking our iPhones with just our faces just now as easy as any causal activity such as patting your hair? While there are diverse technologies available for use and different algorithms underlying many recognition systems, the following are the general step by step processes in every facial recognition application.
In every facial recognition process, the face is detected. This could be from a photo, a video, or a live feed coming straight from a camera.
Next, the face detected is analyzed. The distinct face contours play the biggest part here. The face generally has about 80 major nodal points used in the analysis of the captured face. This step involves a thorough analysis of unique landmarks present on the face, and distance between many parts of the face; such as between the eyes and wideness of jaws and cheeks.
The collected data and analyzed patterns are swiftly converted into suitable mathematical representations, which are otherwise known as faceprints. Just as thumbprints are gotten from thumb impressions, the faceprint is also got and stored.
This is the final step. The derived faceprint is finally compared with the records of faceprints or rather faces in its database until a match is found. The facial database is a robust one/. And half of the United States populations alone have their faces in at least one facial database. It is why the subject of databases itself has raised issues and concerns and government restrictions.
While the technology might have become so ubiquitous in some areas of applications that we do not actively think of the scenario as successful use of facial recognition, there are other scenarios as well boasting of its effective use cases. The following are few of its use cases:
Facial phone locks have grown in popularity over the past few years and mobile phone makers remain one of the most prolific users of facial recognition technology. By implementing Face ID authentication, smartphone security has been improved and brought up a notch.
Facial recognition has been at the heart of many arrests made at airports, and beyond. The technology has also been leveraged in cracking down on illegal stays in the country.
Giant companies such as Facebook and Google have both employed facial recognition filters in tagging unloaded pictures. Google photos and Facebook’s algorithms are both known to have accuracies nearing a hundred percent.
Identification of Genetic Disorders
The versatile technology can also be employed for healthcare cases such as in the identification of genetic disorders. Apps like Face2Gene and DeepGestalt software employ facial recognition to detect a genetic disorder. They analyze faces present in their database and compare them with known faces associated with the disorders.
The technology has been found useful in marking attendance systems and is a good way to know who was present and who is not without intrusion.
Businesses and Companies
Facial recognition also comes into play in companies and businesses, especially in working environments where workers can be identified and verified.
Facial recognition systems that mark individuals as a threat if they have shoplifted can be installed in stores. This system can identify shoplifters and notify appropriate authorities as soon as possible of past offenses, even if their visit is the first in that particular store. The downside of the technology, of course, lies with inaccuracy and harassment of the wrong customers.
The biggest strength of the recognition technology could also prove to be its greatest weakness. While efficient in its matching capabilities, it also has to rely on a sufficient database of faces. And with the supply of data comes the niggling issue of privacy. Are there enough reasons for you to be concerned?
Here are some of the issues surrounding the implementation of facial recognition on a massive scale.
The issue of ownership lingers as you own your face. However, by uploading your photos and videos to social media spaces, the line between and ownership and the violation of those rights thins. Image data is sellable, and you remain traceable.
By giving up image data of yourself, you remain susceptible to hackers. The irony is you are not in charge of the security of your data, and can likely do little about it when a breach is reached.
Wrong or Mistaken Identity
While facial recognition is almost a hundred percent accurate, the 0.28% error rate (as exemplified by Google’s FaceNet), could be just as disastrous. Wrong arrests can be made based on wrong predictions.
Illegal Face recognition apps can be used to stalk users and search potential victims on a facial database without much stress.
One of the cons that come with the technology is the ability of the government to track you, no matter how private and hidden you want to be.
The biggest flaw of the facial recognition system could be the want of laws governing facial recognition systems. Data remains as important as ever, and in this new decade, it just got more important. Should you be doing more to protect your privacy?
In the aspect of facial recognition, there is very little you can do to protect yourself, reaction-wise that is. Privacy, in this case, is better preemptive than reactive. Glasses made available by researchers at Carnegie Mellon University to escape recognition from facial recognition systems, for instance, have been countered by other similar researches even before it hit the markets.
However, regarding possible preemptive measures, here are possible actions you could take:
Modify Social Network Settings
Facebook for one permits you to opt-out of its facial recognition system. While similarly, Google+ won’t turn on facial recognition except you make a decision to opt-in. By leveraging the choice you have, you could be taking your own privacy into your hands.
Use a VPN
Getting yourself a VPN may be the first step to shedding off unwanted trackers and monitoring you don’t want.
The subject of performance in facial recognition technologies is a delicate one. Accuracy is important, and so is trade-off too, an error rate of 2 percent can be just as disastrous despite its size.
Here are the best performing facial technologies in the new decade.
Google’s FaceNet ranks the best-performing recognition system with an accuracy of 99.63 percent, trumping the human performance of 97.53 percent quite emphatically. Google Photos leverages the FaceNet in sorting pictures and automatically tagging recognized faces. The online release of the unofficial open-source version, OpenFace, only underlies the importance and impact of Google’s FaceNet on the biometrics space.
Researchers in Hong Kong made commendable progress when they employed the GaussianFace algorithm in developing a facial recognition system with an accuracy of 98.52 percent. While the method was generally suggested to be non-cost-efficient, the feat was remarkable nonetheless.
Facebook’s impressive DeepFace program has an accuracy of 97.25 percent; just 0.28 percent shy of human performance.
Amazon‘s cloud-based Rekognition service has been touted to be capable of recognizing more than a hundred faces in a single picture. Amazon has since been working on touting Rekognition to law enforcement agencies.
Microsoft, IBM, and Megvii
Microsoft and IBM‘s collaborations yielded a performing but bias system altogether. Error rates in darker-skinned people were higher and Microsoft has since been working on improving its system with collaborations with IBM and a Chinese company, Megvii.
We have made laudable progress with AI innovations over the years, and it is only natural that the valuation of the AI industry, in general, would continue to increase. Facial recognition and Computer Vision, in general, remain one of the most dominant fields to emerge from an AI-inspired world; and the technology, along with its other contributing factors, such as privacy laws and regulations, would only get better with time.