By recognizing the users’ already detected patterns and behavioral data, machines continue to improve their output, which is what the latest AI and its offset technologies like Machine Learning and Deep Learning are doing.
You look at your smartphone screen, and it is unlocked. You need to make a payment through a mobile wallet app, and for authorization, you need to look straight at the device camera, and it’s done. Yes, this is how to face recognition works in our day to day digital interactions. But facial recognition is more than just unlocking your smartphone device or making repayment by showing your face to the camera.
We have already entered the era of face recognition AI, more intelligent, and powerful use of face recognition for a multitude of tasks. If you have seen movies like Minority Report where faces are scrutinized for probable future crimes, you have experienced the technology’s fictional limit. The present disposition of the technology is still limited to recognizing the basic facial characteristics irrespective of outside influences, health problems, and emotional contours.
Face Recognition AI and Safeguarding Privacy
Recognizing people’s faces in actual time has already become a reality now, and the technology is further being utilized by the administrative departments and security forces to create a massive database of people’s faces in different situations. Using such a massive database of faces and expressions and behavioral psychology, the administrative forces are actually progressing to create highly powerful algorithms that can detect certain signs of expression common in people before and after committing crimes.
While such intelligent utilization of AI-powered face recognition will continue and help many purposes in our private and public life, including authentication and security measures, there is a growing concern over the data privacy and unauthorized access to facial recognition data for commercial purposes.
As of now, the face recognition technology has mostly been into focus for the exceptional ease it offers for user authentication across digital platforms. But as face recognition is increasingly getting powerful and is being used beyond the so-called consumer apps, data privacy concerns are increasing. In the time to come, face recognition data will be a bigger subject of privacy and data security concerns.
Just like the GPS and other technologies that ask for user permission for operating in the background, AI-powered intelligent face recognition in the time to come will seek permission and seek user consent for using face recognition and using the corresponding data for authentication and other purposes.
The New Era of Facial Recognition
Over the years, face recognition also not stood still in the same spot as a technology. Instead, it continued to evolve and become more improved and sharpened in capabilities. There have been many pathfinding face recognitions uses and applications in recent years. For example, we have seen apps where you can check several public images of that person across multiple platforms once you upload a face image. Such applications of face recognition technology could only be possible thanks to the huge database of facial data.
In the years to come, many leading brands and digital platforms are going to create their databases loaded with facial data to utilize the technology for targeting customers and users more precisely with promotions and offers. While doing so, Artificial Intelligence (AI) technology will play a decisive role in deciphering the facial expressions signifying user mood, pleasure, displeasure, and common triggers for buying wishes.
Deep Learning Algorithms and Facial Recognition
Now that we have explained so far, the promises and possibilities associated with the face recognition AI technology, it is time to find out the common challenges preventing the implementation of this technology. In fact, still now, the most well-equipped face recognition AI has shown its shortcomings in recognizing the same face correctly in different moods and ambiance. It has been found that a simple change of lighting or makeup or expression can confuse a machine in recognizing a known face. Still, machines are far behind the human capabilities in respect of recognizing faces just because our brain neurons are trained and developed well to recognize faces in spite of changes in expression and other factors.
According to the experts, the most important aspects creating difficulties for machines in recognizing the faces are aging, emotions, illumination, and pose. These four factors mainly contribute to the changing appearances of the faces. These are also the four factors that make algorithms confused while recognizing the faces. To prevent this from happening, now some sensors use a different method. They just segregate the face into different nodal points, such as the difference between the eyes, the difference between the nose and the upper lip, etc. Based on these multiple measurements for every face, a unique code is created. The face recognition app recognizes a face or considers a face stranger based on this unique code’s database.
Conclusion
Lastly, modern face recognition algorithms also enjoy a clear edge over human capabilities. They learn from human errors and adjust their activities for future contexts. If an algorithm mistakenly cannot recognize a known face or confuses between two different faces, it learns from the mistakes. Accordingly, it creates a roadmap using this error data so that in future similar mistakes can be minimized.
By recognizing the users’ already detected patterns and behavioral data, machines continue to improve their output, which is what the latest AI and its offset technologies like Machine Learning and Deep Learning are doing.
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