Sandeep Sharma presented on face recognition. He discussed the history and types of face recognition including 2D and 3D. He explained how face recognition works by measuring facial landmarks and using algorithms like PCA and LDA to analyze features. Challenges included disguises and large crowds. Future uses could include law enforcement, banking security, and airports. Advancements are still needed for widescale deployment.

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.