Enhancing societal security: a multimodal deep learning approach for a public person identification and tracking system

Abstract

In public spaces, threats to societal security are a major concern, and emerging technologies offer potential countermeasures. The proposed intelligent person identification system monitors and identifies individuals in public spaces using gait, face, and iris recognition. The system employs a multimodal approach for secure identification and utilises deep convolutional neural networks (DCNNs) that have been pretrained to predict individuals. For increased accuracy, the proposed system is implemented on a cloud server and integrated with citizen identification systems such as Aadhar/SSN. The performance of the system is determined by the rate of accuracy achieved when identifying individuals in a public space. The proposed multimodal secure identification system achieves a 94% accuracy rate, which is higher than that of existing public space person identification systems. Integration with citizen identification systems improves precision and provides immediate life-saving assistance to those in need. Utilising secure deep learning techniques for precise person identification, the proposed system offers a promising solution to security threats in public spaces. This research is necessary to investigate the efficacy and potential applications of the proposed system, including accident identification, theft identification, and intruder identification in public spaces.