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.

本文研究了无约束耳辨识问题，并证明了当使用深度卷积神经网络模型进行耳辨识时领域适应的重要性。通过收集新的耳朵数据集，混合不同的深度卷积神经网络模型来进一步提高性能，分析了耳像质量对分类性能的影响。通过在 UERC 数据集上进行实验验证，实验结果表明，领域适应会带来显著的性能提升，并且图像质量对结果有影响。对不同数据集存在偏差的问题进行分析，结果表明耳辨识数据集之间存在较强的偏差。

使用深度卷积神经网络进行耳部识别的域自适应

Domain Adaptation for Ear Recognition Using Deep Convolutional Neural  Networks

In this paper we present the results of the Unconstrained Ear Recognition
Challenge (UERC), a group benchmarking effort centered around the problem of
person recognition from ear images captured in uncontrolled conditions. The
goal of the challenge was to assess the performance of existing ear recognition
techniques on a challenging large-scale dataset and identify open problems that
need to be addressed in the future. Five groups from three continents
participated in the challenge and contributed six ear recognition techniques
for the evaluation, while multiple baselines were made available for the
challenge by the UERC organizers. A comprehensive analysis was conducted with
all participating approaches addressing essential research questions pertaining
to the sensitivity of the technology to head rotation, flipping, gallery size,
large-scale recognition and others. The top performer of the UERC was found to
ensure robust performance on a smaller part of the dataset (with 180 subjects)
regardless of image characteristics, but still exhibited a significant
performance drop when the entire dataset comprising 3,704 subjects was used for
testing.

本文介绍了耳朵识别方案 UERC (Unconstrained Ear Recognition Challenge) 的结果，包括集中于未受控制条件下从耳图像中识别人的问题，挑战的目标是评估现有的耳识别技术在具有挑战性的大规模数据集上的性能，并识别未来需要解决的开放问题。