Comparative Study And Analysis Of Quality Based Multibiometric Technique Using Fuzzy Inference System
Biometric is a science and technology of measuring and analyzing biological data i.e. physical or behavioral traits which is able to uniquely recognize a person from others. Prior studies of biometric verification systems with fusion of several biometric sources have been proved to be outstand...
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| 格式: | Thesis |
| 語言: | English |
| 出版: |
2013
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| 主題: | |
| 在線閱讀: | http://eprints.usm.my/43781/1/Lydia%20Binti%20Abdul%20Hamid24.pdf http://eprints.usm.my/43781/ |
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| 總結: | Biometric is a science and technology of measuring and analyzing biological
data i.e. physical or behavioral traits which is able to uniquely recognize a person
from others. Prior studies of biometric verification systems with fusion of several
biometric sources have been proved to be outstanding over single biometric system.
However, fusion approach without considering the quality information of the data
used will affect the system performance where in some cases the performances of the
fusion system may become worse compared to the performances of either one of the
single systems. In order to overcome this limitation, this study proposes a quality
based fusion scheme by designing a fuzzy inference system (FIS) which is able to
determine the optimum weight to combine the parameter for fusion systems in
changing conditions. For this purpose, fusion systems which combine two modalities
i.e. speech and lip traits are experimented. For speech signal, Mel Frequency
Cepstral Coefficient (MFCC) is used as features while region of interest (ROI) of lip
image is employed as lip features. Support vector machine (SVM) is then executed
as classifier to the verification system. For validation, common fusion schemes i.e.
minimum rule, maximum rule, simple sum rule, weighted sum rule are compared to
the proposed quality based fusion scheme. From the experimental results at 35dB
SNR of speech and 0.8 quality density of lip, the EER percentages for speech, lip,
minimum rule, maximum rule, simple sum rule, weighted sum rule systems are
observed as 5.9210%, 37.2157%, 33.2676%, 31.1364%, 4.0112% and 14.9023%,
respectively compared to the performances of sugeno-type FIS and mamdani-type
FIS i.e. 1.9974% and 1.9745%. |
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