International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004
System accuracy vs. No. of eigenfaces
9
1100 100
Se
> 90-7
5
g
2 80 -
o
o
< 70
5
= 60
e
50
-1 1 3 5 7 9
Number of eigenfaces in database
Figure 5: Accuracy base on Eigenfaces
System accuracy vs. No. of face
orientaions
89 +
88.5 |
88
87.5 |
Accuracy
©
co 9 o
O C!
855 |
©
C
84.5
0 5 10 15
Number of face orintations
Figure 6 System Accuracy vs Number of Face orientations
6. CONCLUSION
Face recognition has been an attractive field of research for
engineering, computer vision scientists and security purposes..
Humans are able to identify reliably a large number of faces
and scientists are interested in understanding the perceptual
and cognitive mechanisms at the base of the face recognition
process. Since 1888, many algorithms have been proposed as
a solution to automatic face recognition. Although none of
them could reach the human recognition performance. This paper
presented an algorithm for face recognition by performing PCA on
Wavelet Transform. The Wavelet Transform is used to decompose
the original image into four Wavelet subbands, each with a
different frequency component. PCA is then applied on this
Wavelet to reconstruct the image into vector representation.
Wavelet Transform provides an excellent image decomposition
and texture description. The combination of Wavelet Transform
and PCA gives a better recognition accuracy and significant
performance improvement when the database has large number of
images. It reduces computational load and increases accuracy of
the system. The paper has resulted in an overall success being able
783
to perform reliable recognition in a constrained environment. a
recognition accuracy of 86% has been achieved. While the
problem of recognizing faces under gross variations remains
largely unsolved, a thorough analysis of the strengths and
weaknesses of face recognition using PCA has been presented and
discussed.
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