Biometric recognition using retinal and iris images is a refined and extremely safe technology in the biometrics field. The retina and iris are exclusive and constant functional features of the human eye that can be employed for individual identification. Retinal and iris detection systems are highly well-known for their high exactness and safety. The individuality and constancy of these features make them challenging in order to spoof or replicate. Biometric detection utilizing retinal and iris images improved by deep learning (DL) models has accompanied a novel period of highly exact and effective identity verification. DL models namely convolutional neural networks (CNN) and recurrent neural networks (RNN) employed for feature removal as well as matching, permitting complex and individual patterns of retina and iris to be taken with notable precision. This technique provides enlarged security and reliability. It is precious in numerous applications like access control, border control, healthcare, and mobile device verification while addressing the challenges of flexible lighting situations and accommodating users with eye conditions. This article presents an effective secure biometric retinal iris identification using an orca predator's algorithm with deep learning (SBRIC-OPADL) technique. The main aim of the SBRIC-OPADL technique is to accomplish biometric security using retinal iris images. Primarily, the SBRIC-OPADL technique exploits the Wiener filtering (WF) approach for the removal of noise that exists in the input iris images. Besides, the SBRIC-OPADL technique exploits the EfficientNet model for the extraction of feature vectors. Moreover, the hyperparameter tuning process of the EfficientNet model takes place using OPA. Furthermore, the biometric identification process can be performed by the use of a convolutional autoencoder (CAE). To validate the enhanced biometric detection results of the SBRIC-OPADL technique is tested using the biometric iris dataset. The extensive results highlighted that the SBRIC-OPADL technique reaches better performance over other models.
