Open AccessFeature and Decision Level Fusion in Children Multimodal Biometrics
Author(s) -
Bhavya D. N*.,
H. K. Chethan
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.e6396.018520
Subject(s) - artificial intelligence , biometrics , pattern recognition (psychology) , support vector machine , computer science , k nearest neighbors algorithm , fingerprint (computing) , naive bayes classifier , principal component analysis , histogram , fusion , classifier (uml) , feature (linguistics) , feature vector , iris recognition , machine learning , linguistics , philosophy , image (mathematics)
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.