1. The document discusses face recognition using an eigenface approach, which uses principal component analysis to extract features from a database of faces to generate eigenfaces that can be used to identify unknown faces. 2. The eigenface approach takes into account the entire face for recognition and is relatively insensitive to small changes in faces. It is faster, simpler, and has better learning capabilities compared to other approaches. 3. Some limitations are that accuracy is affected if lighting and face position vary greatly, it only works with grayscale images, and noisy or partially occluded faces decrease recognition performance.

1. Presented By:
M.Divya Sushma
(08PA1A0433)

2. Digital image processing is a rapidly evolving field with growing
applications in science and engineering . Image processing holds the
probability of developing the ultimate machine that could perform
the visual function of all living beings.
Here an approach is made to detect and identify a human
face and describe the algorithm for software implementation of face
recognition system using eigenface. In eigenface method, training
set is prepared first and then the person is recognized by comparing
characteristics of the face to those of known individuals.

3. Face is our primary focous of interaction with society, face
communicates identify, Emotion, race and age. It is also quite
useful for judging gender, size and perhaps even character Of
the person.

4. The major approaches used for face recognition are
1.Featured based approach
2.Eiganface based approach

5. 1.Feature based approach:
First order features values
Second order features values
2. Eigen Face Based Approach:

8. HISTORY:
HOW EIGEN FACES WILL GENERATED:

11. In this section, the original scheme for determination of the
eigenfaces using PCA will be presented. The algorithm
described in scope of this paper is a variation of the one
outlined here.

16. MERITS:
 Complete face information is taken into account for
recognition.
 Relative insensitivity to small or gradual change in
the face image.
 Better in speed , simplicity and learning capability

17. DEMERITS:
 If lighting effects and the position of the face with respect to
the camera is varied Greately then accuracy will effect.
 Only gray scale images can be detected
 A noisy image or partially occluded face causes recognition
performance to degrade gracefully.

18. Face recognition system has following application:
 Given a database of standard face images (say criminal mug
shots), determine whether or not a new shot of a person is in database.
 Authorize users to allow login access.
 Prepare a surveillance camera system residing at some public place which
automatically matches the input faces with criminal database and gives
alert if the results are matched.
 Match the person with his passport image, licence image etc.

20. EIGENFACES
RECONSTRUCTION

21.  Face Recognition has been successfully implemented
using eigenface approach. Eigenface approach of face
recognition has been found to be a robust technique
that can be used in security systems

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 M. Turk and A. Pentland. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3 (1), 1991a.
URL http://www.cs.ucsb.edu/ mturk/Papers/jcn.pdf. (URL accessed on November 27, 2002).
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URLhttp://www.cs.wisc.edu/ dyer/cs540/handouts/mturk-CVPR91.pdf. (URL accessed on November
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