Face recognition


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Face recognition

  1. 1. Presentation by: Sandeep Sharma CSE
  2. 2.  Introduction Types of face recognition How it works Working steps Algorithms used Problems faced Future uses Conclusion
  3. 3.  Inthe 1960s, scientists began work on using the computer to recognize human faces. Since then, face recognition technology has come a long way.A Face recognition software is based on the ability to recognize a face by measuring the various features of the face.
  4. 4.  Every face has numerous distinguishable landmarks,the different peaks and valleys that make facial features. Face recognition system defines these landmarks as nodal points.Each human face has approximately 80 landmarks. Some of them are: • Distance between the eyes • Width of the nose • Shape of the cheek bones • Length of the jaw line
  5. 5.  2D Face Recognition 3D Face Recognition
  6. 6.  In past,face recognition system relied on comparing 2D images with another 2D images in data base. Person should be looking towards the camera. Even the slight variance in light or smile of a person creates the problem. This makes the system less effective.
  7. 7.  Capturing the real time images of the person. Uses distinctive features of face. Can be used in darkness. Has the ability to recognize the person through different angles.
  8. 8.  2D Face Recognition 3D Face Recognition
  9. 9.  Detection:Acquiring an image by scanning or by using a video image. Aligment:Once the face is detected the system identifies the head’s position,size and pose.
  10. 10.  Measurement: The system then measures the curves of the face on a sub-millimeter scale and creates a template. Representation: The system translates the template into a unique code.
  11. 11.  Matching:The 3D image is matched with 3D image in the Database. Verification:In verification the image is verified with the one image in the database and result is displayed side wise.
  12. 12.  Face recognition algorithms identify facial features by landmarks. Some of them are: • Principle Component Analysis(Eigenfaces) • Linear Discriminate Analysis. • Elastic Bunch Graph Matching. • Multilinear Subspace Learning(Tensor)
  13. 13.  The Person in disguise cannot be caught. Less effective in huge crowd.
  14. 14.  Technology is used by Law Enforcement Agencies. Can be used in Banking for identification. Can be used on airport for security purpose.
  15. 15.  At present it is most promising for small or medium scale applications.such as office access and computer log in. It still face great technical challenges for large scale deployments.such as airport security. Advancement in hardware and software needed. Can emerge as Backbone of security system.