Emerging trends in biomedical trait-based human identification: A bibliometric analysis

Abstract

Personal human identification is a crucial aspect of modern society with applications spanning from law enforcement to healthcare and digital security. This bibliometric paper presents a comprehensive analysis of recent advances in personal human identification methodologies focusing on biomedical traits. The paper examines a diverse range of research articles, reviews, and patents published over the last decade to provide insights into the evolving landscape of biometric identification techniques. The study categorizes the identified literature into distinct biomedical trait categories, including but not limited to, fingerprint and palmprint recognition, iris and retinal scanning, facial recognition, voice and speech analysis, gait recognition, and DNA-based identification. Through systematic analysis, the paper highlights key trends, emerging technologies, and interdisciplinary collaborations in each category, revealing the interdisciplinary nature of research in this field. Furthermore, the bibliometric analysis examines the geographical distribution of research efforts, identifying prominent countries and institutions contributing to advancements in personal human identification. Collaboration networks among researchers and institutions are visualized to depict the knowledge flow and collaborative dynamics within the field. Overall, this study serves as a valuable reference for researchers, practitioners, and policymakers, shedding light on the current status and potential future directions of personal human identification leveraging biomedical traits.

Keywords

• Biometric identification
• Pattern recognition
• Traits analysis
• Machine learning
• Deep learning
• Bibliometric