The rectangular tokens common to vision transformer methods for visual recognition can strongly affect performance of these methods due to incorporation of information outside the objects to be recognized. This paper introduces PaW-ViT, Patch-based Warping Vision Transformer, a preprocessing approach rooted in anatomical knowledge that normalizes ear images to enhance the efficacy of ViT. By accurately aligning token boundaries to detected ear feature boundaries, PaW-ViT obtains greater robustness to shape, size, and pose variation. By aligning feature boundaries to natural ear curvature, it produces more consistent token representations for various morphologies. Experiments confirm the effectiveness of PaW-ViT on various ViT models (ViT-T, ViT-S, ViT-B, ViT-L) and yield reasonable alignment robustness to variation in shape, size, and pose. Our work aims to solve the disconnect between ear biometric morphological variation and transformer architecture positional sensitivity, presenting a possible avenue for authentication schemes.

翻译：视觉Transformer方法中常见的矩形标记，由于会纳入待识别对象之外的信息，可能严重影响这些方法的性能。本文提出了PaW-ViT（基于补丁形变的视觉Transformer），这是一种植根于解剖学知识的预处理方法，通过对耳部图像进行归一化来提升ViT的效能。通过将标记边界精确对齐到检测到的耳部特征边界，PaW-ViT获得了对形状、尺寸和姿态变化更强的鲁棒性。通过将特征边界对齐到自然的耳部曲率，它为各种形态生成了更一致的标记表示。实验证实了PaW-ViT在各种ViT模型（ViT-T、ViT-S、ViT-B、ViT-L）上的有效性，并获得了对形状、尺寸和姿态变化的合理对齐鲁棒性。我们的工作旨在解决耳部生物特征形态变化与Transformer架构位置敏感性之间的脱节问题，为身份验证方案提供了一条可能的途径。