Feature and Decision Level Fusion in Children Multimodal Biometrics
Bhavya D. N.1, Chethan H. K.2
1Bhavya D. N., Department of Computer Science, University of Mysore, Karnataka, India.
2Chethan H. K., Professor, Department of Computer Science and Engg, MIT Thandvapura, Karnataka, India.
Manuscript received on January 02, 2020. | Revised Manuscript received on January 15, 2020. | Manuscript published on January 30, 2020. | PP: 2522-2527 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6396018520/2020©BEIESP | DOI: 10.35940/ijrte.E6396.018520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this paper, we design method for recognition of fingerprint and IRIS using feature level fusion and decision level fusion in Children multimodal biometric system. Initially, Histogram of Gradients (HOG), Gabour and Maximum filter response are extracted from both the domains of fingerprint and IRIS and considered for identification accuracy. The combination of feature vector of all the possible features is recommended by biometrics traits of fusion. For fusion vector the Principal Component Analysis (PCA) is used to select features. The reduced features are fed into fusion classifier of K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Navie Bayes(NB). For children multimodal biometric system the suitable combination of features and fusion classifiers is identified. The experimentation conducted on children’s fingerprint and IRIS database and results reveal that fusion combination outperforms individual. In addition the proposed model advances the unimodal biometrics system.
Keywords: Gabor, MR filter, HOG, KNN,SVM,NB
Scope of the Article: Microwave filter.