Biometric Verification System Using Hyperparameter Tuned Deep Learning Model

Abstract

Deep learning (DL) models have been useful in many computer vision, speech recognition, and natural language processing tasks in recent years. These models seem a natural fit to handle the rising number of biometric recognition problems, from cellphone authentication to airport security systems. DL approaches have recently been utilized to improve the efficiency of various biometric recognition systems. Iris recognition was considered the more reliable and accurate biometric detection method accessible. Iris recognition has been an active research region in the last few decades due to its extensive applications, from security in airports to homeland security border control. This article presents a new Political Optimizer with Deep Transfer Learning Enabled Biometric Iris Recognition (PODTL-BIR) model. The presented PODTL-BIR technique recognizes the iris for biometric security. In the presented PODTL-BIR model, an initial stage of pre-processing is carried out. In addition, the MobileNetv2 feature extractor is utilized to produce a collection of feature vectors. The PODTL-BIR technique utilizes a bidirectional gated recurrent unit (BiGRU) model to recognise iris for biometric verification. Finally, the political optimizer (PO) algorithm is used as a hyperparameter tuning strategy to improve the PODTL-BIR technique's recognition efficiency. A wide-ranging experimental investigation was executed to validate the enhanced performance of the PODTL-BIR system. The experimental outcome stated the promising performance of the PODTL-BIR system over other existing algorithms. Show more