Progress and Challenges in Ear Biometrics for Secure Identification
Niraj K. Nagrale, Vishakha Niraj Nagrale · 2025
Ear biometrics is a growing field in biometric recognition, offering a unique and stable alternative for personal identification. Unlike traditional biometric modalities such as fingerprints and facial recognition, ears provide a non-intrusive, stable, and unique feature set that remains relatively unchanged over time. Recent advancements in machine learning and deep learning have significantly improved the accuracy of ear recognition systems. This paper explores various methodologies for feature extraction, including geometric-based, texture-based, and deep learning approaches. Additionally, we discuss recognition algorithms such as Principal Component Analysis (PCA), Support Vector Machines (SVM), and Convolutional Neural Networks (CNNs). The applications of ear biometrics extend to security, forensic science, and healthcare, offering promising potential in identity verification and crime investigation. However, challenges such as dataset limitations, occlusions, and variations due to aging persist. Future research should focus on improving deep learning models, increasing dataset diversity, and exploring multi-modal biometric systems for enhanced security and accuracy. This paper provides an in-depth analysis of the advancements, challenges, and future directions in ear biometrics, paving the way for further innovation and real-world applications.