Multimodal biometric methods have been commonly used by several implementations because of its capability to work with a variety of important drawbacks in unimodal biometric methods, such as noise affectability, populace coverage, intraclass variety, vulnerability to spoofing, and non-universality. In this research, a multimodal biometric realtime method is suggested depending upon the design of a deep learning model for pictures of a person’s (right & left) irises. This system has been implemented by combining the characteristics of convolution neural networks and transfer learning techniques. Through this research, the training system focused on a collection of the back_propagation technique with Adam’s optimization approach utilized to modify weights and adjust learning rates during the learning process. The efficiency of the system is examined on two public datasets obtained in various conditions: IITD and CASIA-Iris-V3 Interval. The implemented system gives an accuracy of 99% for both left & right IITD iris datasets and accuracy of (94% and 93%) for the left and right iris for CASIA-iris-V3 interval datasets respectively after training. An OpenCV library for image pre-processing, Keras, and sci-kit learn python libraries for feature extraction and recognition has been utilized.
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