Category: ML

Face recognition, isometric icon, man look into the phone camera, biometric technology, identification, detection of identity, mobile phone dark neon

AI / ML Based Facial Detection and Recognition

Quick Summary

  • This kind of application provides an easier human-machine interaction routine in concern to the face detection and recognition technology With the help of a regular web camera, a machine can detect and recognize a person’s face from the previously-stored database of the faces. 
  • However, face detection can have very useful applications. 
  • To provide the set of the detection algorithm, behind that algorithm, specific architectures works 
  • These algorithms have got to provide at least a 95% successful cognizance rate, out of which less than 3% of the detected faces are false positives.
  • It will take at least 3-4 years for facial recognition to come in complete correspondence with human rights and one’s privacy.

How Face Detection and Recognition work...?

face Recognition
[Electronically modified images which have been correctly identified]
  • The facial recognition process generally involves two stages

  • The specific processor Algorithm to get the specific, more details about a person’s face.

  • Face Detection where the picture is searched to find a face, then the picture is processed to crop and extract the person’s face for easier recognition.

  • Face Recognition where that detected and processed face is compared with a database of known faces, for outcomes who’s that person.

The barrier for Face Detection and Recognition

face Recognition
[Electronically modified images which have been correctly identified]

 

The problem can be formulated as follows:

  • Live or video images of a scene identify  or  verify  one  or  more  persons in the scene using a stored database of faces.
  • The input to the system is an unknown face and the system reports back the decided identity from a database of known individuals whereas in  verification  problems the system needs to confirm or reject the claimed identity of the input face.
  • Uncontrolled video images posing a wide range of different technical challenges.
  • The simple case with small rotation angles.
  • The most commonly addressed case when there are frontal and rotated images

Face Detection and Recognition Threats and concerns

It can track people like you: anytime, anywhere. But also helps Government to catch criminals.

Machine learning technology requires massive data sets to “learn” to deliver accurate results. So there needs to be enormous storage.

You cannot guarantee that a person will stand still and face the camera, so the results will not always be correct. 

CONCLUSION

face Recognition
  • We hope you learn something new in this AI / ML Based Facial Detection and Recognition Blog.
  • We have to throw out simple and efficient techniques for processing and recognizing face objects and applying algorithms that perform better as per requirement.
  • The characteristics of these techniques are very suitable for many applications.
  •  We first identified two key issues in the face recognition literature
    the illumination and pose problems We then examined existing methods of handling these two problems extensively.
We hope you get something new in this 
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