ppt by sandeep sharma

1. Presentation by:
Sandeep Sharma
CSE

2.  Introduction
 Types of face recognition
 How it works
 Working steps
 Algorithms used
 Problems faced
 Future uses
 Conclusion

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.  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.  2D Face Recognition
 3D Face Recognition

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.  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.  2D Face Recognition
 3D Face Recognition

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.  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.  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.  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.  The Person in disguise cannot be caught.
 Less effective in huge crowd.

14.  Technology is used by Law Enforcement
Agencies.
 Can be used in Banking for identification.
 Can be used on airport for security purpose.

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.