Vein Pattern-Based Partial Finger Vein Alignment and Recognition

IF 5
Enyan Li;Lu Yang;Kuikui Wang;Yongxin Wang;Yilong Yin
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Abstract

Partial finger vein recognition is a challenging but important task in scenarios where the sensors used for user enrollment and recognition differ due to sensor upgrades, and there is a significant disparity in the imaging area between their respective imaging windows. Although the state-of-the-art recognition methods achieve promising performance on full finger vein images, they may suffer from degradation on partial finger vein images. To deal with the problem of recognizing a patch of a finger vein image, this paper proposes a vein pattern-based partial finger vein alignment and recognition method. This method employs the direction variation points as minutiae of finger vein pattern in conjunction with the vein bifurcation points and endpoints to align full and partial images. The process involves a two-stage alignment mechanism, i.e., rough alignment constrained by finger physical structure, and precise alignment determined by joint texture and location features. The candidate matching region(s) can be identified within the full gallery image corresponding to the partial probe image, and further used in subsequent minutiae and vein pattern-based recognition. Gallery images that fail to exhibit minutiae matches are classified as imposters in verification mode, or receive matching scores of zero in identification mode. The extensive experimental results on three finger vein databases demonstrate the advantage of the proposed method in partial finger vein recognition, achieving an accuracies of 97.54% on HKPU-FV, 97.22% on PLUS-LED and 97.22% on PLUS-LAS.
基于静脉模式的手指部分静脉定位与识别
当用于用户注册和识别的传感器由于传感器的升级而不同,并且各自成像窗口之间的成像区域存在显着差异时,部分手指静脉识别是一项具有挑战性但又重要的任务。尽管目前最先进的识别方法在全指静脉图像上取得了很好的效果,但在部分指静脉图像上可能会出现性能下降。针对手指静脉图像的小块识别问题,提出了一种基于静脉模式的手指部分静脉对齐与识别方法。该方法利用方向变异点作为手指静脉形态的细枝末节,结合静脉分叉点和端点对全、局部图像进行对齐。该过程包括两个阶段的对准机制,即由手指物理结构约束的粗糙对准和由关节纹理和位置特征决定的精确对准。候选匹配区域可以在与部分探针图像对应的全画廊图像中识别,并进一步用于随后的基于细节和静脉模式的识别。未能展示细节匹配的图库图像在验证模式下被归类为冒名顶替者,或在识别模式下获得零匹配分数。在三个手指静脉数据库上的大量实验结果证明了该方法在部分手指静脉识别方面的优势,在HKPU-FV、PLUS-LED和PLUS-LAS上的准确率分别达到97.54%、97.22%和97.22%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
10.90
自引率
0.00%
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