Ear Recognition Advancements and Trends: A Scientometrics and Systematic Review

Ear biometrics (EB) appears as a potential solution in the rapidly evolving field of artificial intelligence (AI), embracing the distinct features of the human ear to provide legitimate identification. This innovative technology intricately captures detailed shapes, features, and unique ear traits, optimizing its potential. Its non-intrusive architecture serves as a strong barrier against sophisticated spoofing efforts in addition to assuring a flawless user experience. Employing VOS Viewer, Power BI, and Python’s Matplotlib and Seaborn libraries, our scientometric study brings into focus the significant advancements achieved in EB, spanning from 2000 to the current era. Various methodologies, such as Machine Learning (ML) and Deep Learning (DL), are addressed in this study, which highlights the underlying concepts of this expanding topic. Analyzing existing methodologies, pinpointing research gaps, and exploring opportunities for enhancement offer valuable insights into the current status and potential advancements in the field.