The New Era of Facial Recognition

Over the years, facial recognition as a technology has not stood still in the same spot. Instead, it continues to evolve and become more improved and sharpened in capabilities. 

There are many pathfinding facial recognition features and applications for many years. For example, we saw several applications where you find several public images of that particular person across various platforms. 

Once you upload a facial image through face recognition technology, it could only take a massive facial input database.

In these upcoming years, many leading brands and digital platforms create their databases loaded with facial data to utilize this technology for targeting clients and users precisely with CTA (Call-to-action). 

While doing, the Artificial Intelligence (AI) technology will play a significant role in decoding its facial expression signifying user mood, pleasure, displeasure, and common triggers for buying wishes.

Deep Learning Algorithms and Facial Recognition

Now that we have explained the promises and possibilities associated with the facial recognition of AI technology. Now, it’s time to find out the common challenges preventing the implementation of this technology. 

Still, the most well-equipped face recognition AI has shown shortcomings in identifying the same face correctly in different moods and feelings. 

It has been known that a simple change of lighting or makeup or expression confuses a machine identifying familiar facial expressions. 

Still, machines that are far behind the human capabilities regarding identifying different faces as our brain neurons are trained and well developed to recognize faces despite changes in expression and other factors.

The essential aspects creating difficulties for machines in recognizing the faces are

  • Aging
  • Emotions
  • Illumination
  • Posing.

These are the four factors that mainly contribute to the changing appearances of the faces.

These are also the four factors that make algorithms confused while recognizing the faces.

It happens to prevent; some sensors now use a different method. They segregate the face into different nodal points, such as the difference between the eyes, the difference between the nose and the upper lip. 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.


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 to minimize similar mistakes in the future.

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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