Integrating handcrafted features with deep convolutional neural network and BWOA optimization for improved postmortem iris recognition system

A postmortem iris recognition system is a specialized biometric identification system used to identify individuals based on their iris patterns after death. Such a system requires a set of specific requirements to ensure its effectiveness and accuracy. So, with this objective, this paper proposes a postmortem iris recognition system approach, which aims to accurately identify individuals based on their iris patterns even after death. The approach utilizes a specialized dataset of postmortem cases, including careful extraction of the iris and accounting for postmortem changes. In addition, the proposed system uses handcrafted features and deep learning techniques to enhance identification accuracy. The system has been tested on a dataset of postmortem iris images, and results show high accuracy in identifying individuals. This approach has potential applications in forensic investigations, disaster victim identification, and identification of remains in mass graves. In addition, the proposed system offers a valuable tool for identifying individuals even in challenging postmortem cases, contributing to biometrics and forensic science.