研究左右耳对称性如何影响深度耳部特征学习效果
How Does Bilateral Ear Symmetry Affect Deep Ear Features?
- 用自动分类器区分左右耳图像，分别训练测试
- 跨数据集实验显示分开处理可显著提升识别准确率
- 为大规模耳识别系统训练提供实用优化建议
耳识别因其人耳的独特性已成为可靠的生物特征技术。随着大规模数据集的可用性提高，卷积神经网络（CNN）已广泛用于直接从原始耳部图像中学习特征，优于传统手工设计方法。然而，近期研究较少关注双边耳对称性对CNN所学特征的影响。本文探讨了双边耳对称性对基于CNN耳识别性能的影响。为此，我们首先构建了一个耳侧分类器，自动将耳部图像分为左耳或右耳。随后，我们研究在训练和测试阶段引入该耳侧信息的效果。在五个数据集上进行了跨数据集评估。结果表明，在训练和测试中分别处理左右耳可带来显著性能提升。此外，针对对齐策略、输入尺寸及多种超参数设置的消融实验，为在大规模数据集上训练基于CNN的耳识别系统以获得更高验证率提供了实际洞见。
原文摘要 · Abstract (English)
Ear recognition has gained attention as a reliable biometric technique due to the distinctive characteristics of human ears. With the increasing availability of large-scale datasets, convolutional neural networks (CNNs) have been widely adopted to learn features directly from raw ear images, outperforming traditional hand-crafted methods. However, the effect of bilateral ear symmetry on the features learned by CNNs has received little attention in recent studies. In this paper, we investigate how bilateral ear symmetry influences the effectiveness of CNN-based ear recognition. To this end, we first develop an ear side classifier to automatically categorize ear images as either left or right. We then explore the impact of incorporating this side information during both training and test. Cross-dataset evaluations are conducted on five datasets. Our results suggest that treating left and right ears separately during training and testing can lead to notable performance improvements. Furthermore, our ablation studies on alignment strategies, input sizes, and various hyperparameter settings provide practical insights into training CNN-based ear recognition systems on large-scale datasets to achieve higher verification rates.
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