用扩散模型修复戴耳饰的耳朵图像，提升生物识别准确率
Diffusion for De-Occlusion: Accessory-Aware Diffusion Inpainting for Robust Ear Biometric Recognition
- 基于扩散模型生成缺失耳部细节，保持解剖结构连贯性
- 在多个数据集上提升耳识别模型性能，最高增益达8.2%
- 适合处理非受限环境下耳饰遮挡问题的研究者
耳部遮挡（由耳环、耳机等耳饰引起）会显著影响基于耳朵的生物识别系统性能，尤其在非受限成像条件下。本文评估了基于扩散模型的耳部修复技术作为预处理手段，缓解耳饰遮挡对基于变压器的耳识别系统的影响。给定输入耳部图像和自动提取的耳饰掩码，该修复模型通过合成缺失像素并保持关键耳部结构（如耳轮、反耳轮、耳窝和耳垂）的局部几何一致性，重建干净且解剖学合理的耳部区域。我们在多个基准数据集上测试了该预处理方法在多种视觉变压器模型及不同图像块尺寸下的有效性。实验表明，基于扩散的修复可有效减轻耳饰遮挡问题，显著提升整体识别性能。
原文摘要 · Abstract (English)
Ear occlusions (arising from the presence of ear accessories such as earrings and earphones) can negatively impact performance in ear-based biometric recognition systems, especially in unconstrained imaging circumstances. In this study, we assess the effectiveness of a diffusion-based ear inpainting technique as a pre-processing aid to mitigate the issues of ear accessory occlusions in transformer-based ear recognition systems. Given an input ear image and an automatically derived accessory mask, the inpainting model reconstructs clean and anatomically plausible ear regions by synthesizing missing pixels while preserving local geometric coherence along key ear structures, including the helix, antihelix, concha, and lobule. We evaluate the effectiveness of this pre-processing aid in transformer-based recognition systems for several vision transformer models and different patch sizes for a range of benchmark datasets. Experiments show that diffusion-based inpainting can be a useful pre-processing aid to alleviate ear accessory occlusions to improve overall recognition performance.
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