%0 Journal Article %1 noauthororeditor %A M, Somashekhar B %A Y.S.Nijagunarya, %D 2018 %J International Journal of Computer Science & Engineering Survey (IJCSES) %K Machine Support Vector %N 6 %T Face and Fingerprint Fusion System for Identity Authentication Using Fusion Classifiers %U http://airccse.org/journal/ijcses/current2018.html %V 9 %X In this work, we propose a feature level fusion and decision level fusion of face and fingerprint for designing a multimodal biometric system. Initially, Gabor and Scale Invariant Feature Transform features are extracted for both offline face and fingerprint of a person and studied the identification accuracy. Later the fusion of the biometric traits is recommended at feature level using all possible combinations of feature vectors. The possible combination of features is fed into fusion classifier of K-Nearest Neighbour(KNN), Support Vector Machine (SVM), Navie Bayes(NB) and Radial Basis Function(RBF). The best combination of feature vectors and fusion classifiers is identified for the proposed multimodal biometric system. Experiments are conducted on Face database and fingerprint database to assess the actualadvantage of the fusion of these biometric traits, in comparison to the unimodal biometric system. Experimental results reveal that fusion combination outperforms individual.

@article{noauthororeditor, abstract = {In this work, we propose a feature level fusion and decision level fusion of face and fingerprint for designing a multimodal biometric system. Initially, Gabor and Scale Invariant Feature Transform features are extracted for both offline face and fingerprint of a person and studied the identification accuracy. Later the fusion of the biometric traits is recommended at feature level using all possible combinations of feature vectors. The possible combination of features is fed into fusion classifier of K-Nearest Neighbour(KNN), Support Vector Machine (SVM), Navie Bayes(NB) and Radial Basis Function(RBF). The best combination of feature vectors and fusion classifiers is identified for the proposed multimodal biometric system. Experiments are conducted on Face database and fingerprint database to assess the actualadvantage of the fusion of these biometric traits, in comparison to the unimodal biometric system. Experimental results reveal that fusion combination outperforms individual. }, added-at = {2019-06-12T10:47:09.000+0200}, author = {M, Somashekhar B and Y.S.Nijagunarya}, biburl = {https://www.bibsonomy.org/bibtex/2daf8db4b63cf0c8cf33ad2818855a271/ijcses}, interhash = {97ba7dc159945c4d159a664971c53345}, intrahash = {daf8db4b63cf0c8cf33ad2818855a271}, journal = {International Journal of Computer Science & Engineering Survey (IJCSES)}, keywords = {Machine Support Vector}, month = {August}, number = 6, timestamp = {2019-06-12T10:47:09.000+0200}, title = {Face and Fingerprint Fusion System for Identity Authentication Using Fusion Classifiers}, url = {http://airccse.org/journal/ijcses/current2018.html}, volume = 9, year = 2018 }