Feature Level Fusion Algorithm for Iris and Face

Abstract

Some Bi-modal or multimodal recognition systems do not contain rich information needed for identification because information supplied to the biometric classifier are consolidated oncethe conclusions of the matching algorithm have been acquired. Feature based Fusion algorithm has the distinction of having richer information due to the integration of the extracted information before the application of the classifiers. Support Vector Machine over time has shown its unbeatable classification of the biometrics characteristics over other supervised learning classifiers due to its ability to minimize the structural risk simultaneously with bound on the margin complexity and by being solved using a quadratic optimization problem. Neural Network in contrast is a non-parametric estimator which is robust to errors in the training data used for classification and regression. Therefore in this research, algorithms for feature extraction of iris and face for recognition is designed; a recognition system using SVM and Multilayer Perceptron (MLP) is also designed based on the extracted features and the designed model is implemented using MATLAB