Efficient Human Iris Recognition Using Adaptive Central Force Optimization Based Anfis Classification

*Suleiman Salihu Jauro, Raghav Yadav

These days, Iris recognition (IR) is a technique of biometric verification of the individual authentication process centered upon the human iris unique pattern that is implemented to control system intended for high security. The recognition of a person centered on iris pattern is attaining more fame owing to the uniqueness of the pattern amongst the people. This work proposed an effective human IR utilizing circle centered iris segmentation and optimized ANFIS classifier. In the previous work, the circle grounded iris segmentation process is done. Here, take the circle based iris segmented images as the input. Then, the Log Gabor, GLAC, Contour-let transform, LGXP, and Canny Edge Detection features are extracted. Thirdly, the extorted features are specified to the ANFIS for achieving the training process. Amid the training process, the ANFIS parameters are simultaneously optimized by the ACFO. The optimized parameters in ANFIS by ACFO efficiently perform the IR process. At last, the recognized iris images are found. The evaluation was carried out at the end of the proposed work using CASIA-V3-Interval, MMU1, along with UBIRIS 1.0 Database. Experimental results compared the optimized ANFIS classifier with the existing SVM, NN, and also NB classifiers concerning precision, sensitivity, NPV, specificity, accuracy, PPV, FPR, FDR, F-measure, MCC, recall. Outcomes exhibited that the proposed optimized ANFIS classifier gives the greatest recognition accuracy.

Volume 11 | 02-Special Issue

Pages: 88-104