X-ray insights: innovative person identification through Siamese and Triplet networks
Farah Hazem, B. Akram, Osamah Ibrahim Khalaf, Ratul Sikder, Sameer Saadoon Algburi · IET conference proceedings. · 2024
The prospective utilizations of medical imaging data for the dependable identification and authentication of individuals have garnered considerable attention in both security and healthcare realms. This significance becomes particularly pronounced in scenarios involving disasters, where conventional means of human identification prove ineffective. Amid such circumstances, Chest X-rays emerge as a valuable resource, capturing the distinct anatomical features of an individual's rib cage, lungs, and heart. These features, inherently unique, stand as steadfast markers for identification, even when the human body is compromised. Given these premises, the integration of Artificial Intelligence techniques, particularly deep learning methodologies, proves exceptionally beneficial in discerning distinctive identifiers within chest X-ray images. This study capitalizes on the potential inherent in a deep learning paradigm, specifically the Siamese network and Triplet loss, presenting an innovative approach to person identification through chest X-ray images. While prior research in this domain has predominantly relied on Convolutional Neural Networks (CNNs) and conventional machine learning methods, our work introduces a distinctive framework by advocating the utilization of Siamese and Triplet networks for this pivotal task. This contribution expands the existing body of knowledge by pioneering a novel methodology that harnesses the Siamese and Triplet loss deep learning architecture to enhance the processing of Chest X-ray images. The implications of our work reverberate significantly in the domains of security and healthcare, where the refinement of dependable and precise identification techniques holds paramount importance, particularly in emergency scenarios.