PCA analysis with Face Recognition

1. Real Time Image/Video Processing with
Applications in Face Recognition
Presented By :
Kamal Singh
M.Tech 1511MC07
IIT Patna
Guided By :
Dr. Jimson Mathew
Associate Professor
IIT Patna

2. Outline
 Introduction
 Problem & Approach
 Principal Component Analysis
 PCA for Classification
 Result
 References

3. Introduction
With development of information technology , biometric identification
technology has attracted the more attention especially human face detection
or recognition , finger print identification , iris identification
 Face recognition has become a popular area of research in computer vision
and one of the most successful applications of image analysis and
understanding
 Nature of the problem, not only computer science research area but it also
include many other area like neuroscience and psychology
The goal is to implement the system for a particular face and
distinguish it from a large number of stored faces with some real-time
variations


4. Problem and Approach
 To face recognition the faces of different person, and can
identify with face matching, to detect the matching of face
implementing the most popular statistical approach Principal
Component analysis.
 Implementation of face recognition based on Principal
Component Analysis

5. What PCA ?
Principle :
Linear projection method to reduce the number of
dimensions.
 Transfer a set of correlated variables into a new set of map
the data into a space of lower dimensionality.
 Map the data into a space of lower dimensionality.
Properties :
 It can be viewed as a rotation of the existing axes to new
positions in the space defined by original variables .
 New axes are orthogonal and represent the directions with
maximum variability.

6. Dimensionality Reduction
 Lose some information
 n dimensions in original data
 Calculate n eigenvectors and eigenvalues
 Choose only the first p eigenvectors, based on their eigenvalues
 Final data set has only p dimensions

7. PCA Working

8. PCA for Face Recognition
MX N
Image
MN X 1 Vector

9. Face Image Matrix
I1
I2
I3
.
.
.
In
CX = 1/(n – 1)*XX^T

10. Covariance Matrix
C =














)(..........
........)(
........)(
21
2221
1211
ppp
p
p
xv),xc(x),xc(x
),xc(xxv),xc(x
),xc(x),xc(xxv

11. Principal Eigenface Matrix

12. Results
Correctly faces match = 6
Wrongly faces match = 2

15. Conclusion
Face matching accuracy in experiment
Acc = ( Correctly faces matched )*100
( Total number of test image )
Acc = (6*100) = 75%
8
Wrongly Faces matched = (2*100)/8 = 25 %

16. References
1. Belhumeur P. N., Hespanha J. P., and Kriegman D. J. 1997,
“Eigenfaces versus fisherfaces: recognition using class specific
linear projection”, IEEE Trans. Pattern Anal. Mach. Intell., vol. 23,
no. 7, pp. 711-720.
2. Eigenfaces face recognition,
https://blog.cordiner.net/2010/12/02/eigenfacesface-recognition-
matlab/. [Online]. Available: Eigenfacesfacerecognition,https:
//blog.cordiner.net/2010/12/02/eigenfaces-face-recognition-
matlab
3. Belhumeur P. N., Hespanha J. P., and Kriegman D. J. 1997,
“Eigenfaces versus fisherfaces: recognition using class specific
linear projection”, IEEE Trans. Pattern Anal. Mach. Intell., vol. 23,
no. 7, pp. 711-720

17. 4 Turk M. and Pentland A. 1991, “Eigenface for recognition”, J.
Cognitive Neuroscience, vol. 3, no. 1, pp. 71–86.
5 Turk M. and Pentland A. 1991, “Eigenface for recognition”, J.
Cognitive Neuroscience, vol. 3, no. 1, pp. 71–86 .
6 Shemi P M, Ali M A, A Principal Component Analysis
Method for Recognition of Human Faces: Eigenfaces
Approach, International Journal of Electronics
7 Communication and Computer Technology(IJECCT),Volume 2
Issue 3 (May 2012).