IJCSI International Journal of Computer Science Issues, Vol. 2, 2009 52
Fig.2. Canonical form based Palm images.
(a) Original image (b) Grey image (c) Resized image (d) Normalized
modal image (e) Diagonalization image
Fig.3. Canonical form based Face images.
(a) Original image (b) Grey image (c) Resized image
(d) Normalized modal image (e) Diagonalization image
The multimodal system has been designed at multi-
classifier & multimodal level. At multi-classifier level,
multiple algorithms are combined better results. At first
experimental the individual systems were developed and
tested for FAR, FRR & accuracy. Table1 shows FAR,
FRR & Accuracy of the systems.
Table1: The Accuracy, FAR, FRR of face & palmprint
Trait |
Ajyrithm |
FAK |
FKK |
Accuraity |
Face |
Canonical |
4.5% |
8.7% |
97% |
PaJmpntit |
1.5% |
2,L∣⅞ |
96% |
In the last experiment both the traits are combined at
matching score level using sum of score technique. The
results are found to be very encouraging and promoting
for the research in this field. The overall accuracy of the
system is more than 97%, FAR & FRR of 2.4% & 0.8%
respectively.
6 Conclusion
Biometric systems are widely used to overcome the
traditional methods of authentication. But the unimodal
biometric system fails in case of biometric data for
particular trait. Thus the individual score of two traits
(face & palmprint) are combined at classifier level and
trait level to develop a multimodal biometric system. The
performance table shows that multimodal system performs
better as compared to unimodal biometrics with accuracy
of more than 98%.
References
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Nageshkumar M., graduated in Electronics and
IJCSI