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Facebook Abandons Facial Recognition, Will No Longer Identify Users to Tag in Uploaded Images

Face recognition is the technology of verifying a subject through an image, video, or any audiovisual element of his or her face. Generally, this identification is used in many areas in order to access an application, system, or any services like security purposes, to find criminals, in examinations and even we can differentiate twins also.  One of the most important murder cases of Ossama binladen was proven caught the criminal by using this technology. So, facial recognition is that much-advanced technology. Identifying a person after analyzing a photograph. It is done by applying a lot of mathematical algorithms and calculations. 

The view of a person from a different side will be different in order to avoid this change; it follows 360 rotation degree analysis(or 3D analysis). so, there will be a low error rate like 0.2 %. As far as Facebook, the Facial recognition software identifies the people in the pictures uploaded in Facebook and suggests users tag these people in the photographs so that they will create a link between the people through their profiles(simply termed as the tagging feature of Facebook).

 

As we know facebook is now one of the largest repositories of digital photographs in the world. In the US, the law enforcement has helped policing, and has prevented mistaken arrest to some extent. Now the facial recognition system has become a topic for discussion as it can be misused by the government, the law enforcement and companies. 

Regarding the privacy concerns, government investigations,a class action lawsuit and regulatory woes Facebook is going to drop the concept of facial recognition system. In order to prevent potential abuse, some cities and states have banned or limited the use of this technology.

Facial recognition is also called face detection or facial detection and it is a technology-based on artificial intelligence which is used to find and identify faces in digital images for various applications like to provide surveillance and tracking of people in real-time. It has progressed from many advanced technologies like artificial neural networks and machine learning and it plays a vital role as the first step in many important applications which use facial recognition technology. And we can say that facial recognition is one of the methods of face detection. 

Anyway, there are some major advantages and disadvantages to this facial recognition.

Advantages of facial recognition System 
  • One of the important advantages of facial recognition systems is their highly improved security. The arrival of Facial recognition strengthens the security in many fields like airports and banks quicker and more accurately than people can.
  • It made it easy and efficient to shopping by using face recognition and using cash payments. 

  • Only a few human interactions is required so physical contact can be reduced and it is very important in the covid pandemic situation.
  • It plays an important role in the medical department, that is it can easily detect genetic disorders. Compared to traditional genetic testing facilities this technology is less expensive and faster in performance. 
  •  The arrival of facial recognition protects businesses against theft like Business owners use software and security cameras to identify known or suspected thieves. 
Disadvantages of facial recognition System

As with any kind of technology, there will be some drawbacks like that the facial recognition system has some cons. Violations of rights and personal freedoms, privacy issues, potential data theft, etc will lead to a risky situation. Just know what are the different drawbacks of facial recognition.

  • Personal privacy threat is the most important negative side of this technology.
  • Facial recognition is used to spying on citizen in the countries like where limited personal freedom
  • This technology is not so perfect
  • The face from a different angle may treat as a different person so facial recognition can be fooled 

 

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