![](bg6.png)
our system computationally very efficient. All
experiments are carried out on MATLAB R2013a,
Windows 7 OS, Intel Core i3 2310M processor with
4GB RAM.
Table 2: Computation time (in seconds) on AT&T
database.
Method
Feature
extraction time
of 400 images (s)
Classification time
per fold (s)
Overall
time (s)
PZM
227.21
0.016
227.226
Proposed
Method
18.07 18.086
6 CONCLUSIONS
Recognizing faces in varying illumination, pose, and
expression condition with computational efficiency
is the most difficult problem of today’s face
recognition systems. An efficient face recognition
system should be able to cope with all of these
problems. In this paper, a novel lower order PZM
based method is presented which can efficiently
recognize faces regardless of illumination, pose, and
expression change. Due to optimal choice of features
the method obtains much better recognition rate with
less computation time. Extensive experimentation
confirms the high recognition rate, computational
efficiency, and robustness of the proposed method
under varying conditions. We believe that the
proposed method has a very good potential to cope
with the real challenges of current face recognition
systems. Future works include analyzing the
performance of the proposed method for other
biometric recognition applications such as
recognition of ear, palmprint etc.
ACKNOWLEDGEMENTS
Authors would like to thank NSERC and URGC
Seed grant for partial support of this project.
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