Deep Learning Approach for Ear Recognition and Longitudinal Evaluation in Children
This work addresses ear recognition for children, a domain-specific problem, but is incremental as it applies existing methods to new data without major methodological breakthroughs.
This paper tackled the problem of ear recognition in children, who experience rapid ear changes with age, by introducing a longitudinal dataset from children aged 4 to 14 over 2.5 years and evaluating performance using a deep learning ensemble of VGG16 and MobileNet, achieving results that highlight challenges in this demographic.
Ear recognition as a biometric modality is becoming increasingly popular, with promising broader application areas. While current applications involve adults, one of the challenges in ear recognition for children is the rapid structural changes in the ear as they age. This work introduces a foundational longitudinal dataset collected from children aged 4 to 14 years over a 2.5-year period and evaluates ear recognition performance in this demographic. We present a deep learning based approach for ear recognition, using an ensemble of VGG16 and MobileNet, focusing on both adult and child datasets, with an emphasis on longitudinal evaluation for children.