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2(1), (2023):33-37 DOI: https://doi.org/10.46632/jdaai/2/1/6
M. Kokila, G. Amalredge, S. Poornima, M. Geethanjali.
Due to their capacity to overcome a number of significant shortcomings in unimodal biometric approaches, such as noise affectability, populace coverage, intraclass diversity, etc., multimodal biometric methods have been widely adopted by many implementations. Non-universality and spoofing vulnerability Based on the building of a deep learning model for images of a person’s (right & left) irises, a multimodal biometric real-time technique is proposed in this study. The features of transfer learning methods and convolution neural network characteristics have been combined to create this system. Through this research, the back-propagation technique was the training system of choice, with Adam’s optimization approach being employed to change weights and alter learning rates as the learning process progressed. Two publicly available datasets are gathered to evaluate the system’s effectiveness.
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M. Kokila, G. Amalredge, S. Poornima, M. Geethanjali. “Convolutional Neural Network For Iris Recognition.” REST Journal on Data Analytics and Artificial Intelligence 2(1), (2023):33-37.