Downsampling is critical for coding-based methods to reduce storage and accelerate matching speed. In coding-based palmprint recognition methods, the image size of the region of interest is typically 128 × 128, which is divided into 32 × 32 blocks and each block consists of 4 × 4 pixels. In the traditional downsampling method, the upper-left pixel in each block is selected to represent the feature of this block. However, this crude technique likely leads to serious information loss. The feature template heavily depends on the upper-left pixels, which degrades the tolerance for pixel-level dislocation and rotation. The authors analyse the downsampling stage in depth, and propose a democratic voting downsampling method (DVDM), which can improve the robustness and accuracy of the coding-based palmprint recognition methods without any prior knowledge. All the pixels in each block have equal voting rights to determine the feature of this block, so DVDM can extract stable features and effectively overcome the autocracy of an upper-left pixel. The sufficient experiments tested on the public PolyU palmprint dataset to confirm that DVDM can remarkably improve the robustness to pixel-level dislocation and rotation, and also improve accuracy performance, equal error rates of the coding-based methods are dropped down at most 11.5%.

中文翻译：

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基于编码的掌纹识别的民主投票缩减采样

下采样对于基于编码的方法以减少存储并加快匹配速度至关重要。在基于编码的掌纹识别方法中，感兴趣区域的图像大小通常为128×128，分为32×32块，每个块由4×4像素组成。在传统的降采样方法中，每个块的左上像素均被选中以代表该块的特征。但是，这种粗糙的技术可能会导致严重的信息丢失。特征模板在很大程度上取决于左上像素，这降低了像素级位错和旋转的容忍度。作者深入分析了下采样阶段，并提出了一种民主投票下采样方法（DVDM），该方法可以提高基于编码的掌纹识别方法的鲁棒性和准确性，而无需任何先验知识。每个块中的所有像素都具有相等的投票权以确定该块的特征，因此DVDM可以提取稳定的特征并有效地克服左上像素的专制性。在公共PolyU掌纹数据集上进行的足够实验测试，证实DVDM可以显着提高像素级位错和旋转的鲁棒性，还可以提高准确性，基于编码的方法的均等错误率最多下降11.5％。