# Domain Adaptation for Ear Recognition Using Deep Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1803.07801 · HTML: https://mlsanity.com/paper/1803.07801/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2018.

## Built on by (most cited first, 5 of 5)

- [Improved Ear Verification with Vision Transformers and Overlapping Patches](https://mlsanity.com/paper/2503.23275/) (2025)
- [Advancing 3D finger knuckle recognition via deep feature learning](https://mlsanity.com/paper/2301.02934/) (2023)
- [Multimodal Personal Ear Authentication Using Smartphones](https://mlsanity.com/paper/2103.12575/) (2021)
- [ProtoN: Prototype Node Graph Neural Network for Unconstrained Multi-Impression Ear Recognition](https://mlsanity.com/paper/2508.04381/) (2025)
- [PaW-ViT: A Patch-based Warping Vision Transformer for Robust Ear Verification](https://mlsanity.com/paper/2601.19771/) (2026)

## Abstract (arXiv)

In this paper, we have extensively investigated the unconstrained ear recognition problem. We have first shown the importance of domain adaptation, when deep convolutional neural network models are used for ear recognition. To enable domain adaptation, we have collected a new ear dataset using the Multi-PIE face dataset, which we named as Multi-PIE ear dataset. To improve the performance further, we have combined different deep convolutional neural network models. We have analyzed in depth the effect of ear image quality, for example illumination and aspect ratio, on the classification performance. Finally, we have addressed the problem of dataset bias in the ear recognition field. Experiments on the UERC dataset have shown that domain adaptation leads to a significant performance improvement. For example, when VGG-16 model is used and the domain adaptation is applied, an absolute increase of around 10% has been achieved. Combining different deep convolutional neural network models has further improved the accuracy by 4%. It has also been observed that image quality has an influence on the results. In the experiments that we have conducted to examine the dataset bias, given an ear image, we were able to classify the dataset that it has come from with 99.71% accuracy, which indicates a strong bias among the ear recognition datasets.