Vein texture elements in images are sometimes degraded due to centrally symmetric blur, e.g. due to motion of hand or camera not being in focus, which significantly affects recognition systems performance. Multiscale local phase quantisation (MLPQ) can effectively improve system accuracy in the presence of blur by dividing vein images into non-overlapping blocks and extracting multiscale local phase information. However, MLPQ misses some important discriminant information at the intersections of different regions, which leaves room for improving the system performance. In the present work, a multiscale overlapping blocks local phase quantisation (MOLPQ) histogram algorithm is presented to divide the vein image into overlapping blocks and extract multiscale local phase information, which includes the discriminant information lost in MLPQ. MOLPQ is validated via comparison with state-of-the-art recognition algorithms on a normal hand vein database, artificial-blur databases, and a normal-blur database, and the accuracies on the normal hand vein and normal-blur databases are 99.57 and 98.07%, respectively. Additionally, MOLPQ outperforms other methods on all databases in terms of accuracy.

中文翻译：

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用于识别模糊手背静脉图像的改进的多尺度局部相位量化直方图

图像中的静脉纹理元素有时会由于中心对称模糊而退化，例如由于手或相机的运动未聚焦，这会显着影响识别系统的性能。多尺度局部相位量化 (MLPQ) 通过将静脉图像划分为不重叠的块并提取多尺度局部相位信息，可以在存在模糊的情况下有效提高系统精度。然而，MLPQ 在不同区域的交叉点遗漏了一些重要的判别信息，这为系统性能的提升留下了空间。在目前的工作中，提出了一种多尺度重叠块局部相位量化（MOLPQ）直方图算法，将静脉图像划分为重叠块并提取多尺度局部相位信息，其中包括 MLPQ 中丢失的判别信息。MOLPQ通过在正常手静脉数据库、人工模糊数据库和正常模糊数据库上与最先进的识别算法进行比较验证，在正常手静脉和正常模糊数据库上的准确率分别为99.57和分别为 98.07%。此外，MOLPQ 在准确性方面优于所有数据库的其他方法。