Domain Adaptation for Ear Recognition Using Deep Convolutional Neural Networks

TL;DR

This paper investigates domain adaptation for ear recognition using deep CNNs, introduces a new dataset, and demonstrates significant performance improvements and dataset bias issues.

Contribution

It presents a new ear dataset, analyzes domain adaptation effects, and combines multiple CNN models to enhance ear recognition accuracy.

Findings

Domain adaptation improves recognition performance by around 10%.

Combining CNN models increases accuracy by 4%.

High dataset bias with 99.71% classification accuracy among datasets.

Abstract

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.