# Deep Learning Approach for Ear Recognition and Longitudinal Evaluation in Children

arXiv: https://arxiv.org/abs/2408.01588 · HTML: https://mlsanity.com/paper/2408.01588/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 30 references that are in the map.
- First posted on arXiv 2 Aug 2024.

## Builds on (most cited first, 5 of 5)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Ear Recognition: More Than a Survey](https://mlsanity.com/paper/1611.06203/) (2016)
- [Employing Fusion of Learned and Handcrafted Features for Unconstrained Ear Recognition](https://mlsanity.com/paper/1710.07662/) (2017)

## Built on by (most cited first, 2 of 2)

- [Evaluating Deep Learning-Based Face Recognition for Infants and Toddlers: Impact of Age Across Developmental Stages](https://mlsanity.com/paper/2601.01680/) (2026)
- [Mitigating Longitudinal Performance Degradation in Child Face Recognition Using Synthetic Data](https://mlsanity.com/paper/2601.01689/) (2026)

## Abstract (arXiv)

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.