Local feature approach to dorsal hand vein recognition by Centroid-based Circular Key-point Grid and fine-grained matching
Local feature approach to dorsal hand vein recognition by Centroid-based Circular Key-point Grid and fine-grained matching
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基于质心的圆形关键点网格和细粒度匹配的手背静脉识别局部特征方法
DOI：
10.1016/j.imavis.2016.07.001
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发表时间：
2017-02
影响因子：
4.7
通讯作者：
Wang Yunhong
中科院分区：
文献类型：
--
作者：
Huang Di;Zhang Renke;Yin Yuan;Wang Yiding;Wang Yunhong
由于局部特征匹配在手背静脉识别的性能和鲁棒性方面都取得了很大的进步，本文通过改进SIFT框架的两个主要步骤，即关键点检测和匹配，提出了一种新颖有效的手背静脉识别方法。对于前者，提出了一种新的关键点生成模式，即基于质心的圆形关键点网格（CCKG），它有效地定位了一定数量的点在手背上的后续SIFT特征提取，导致歧视性的描述。与现有的关键点检测器相比，CCKG全面考虑了手背的属性，包括静脉网络以及周围的真皮区域，因此实现了良好的代表性和低复杂度。对于后者，一个细粒度的匹配过程中引入了多任务稀疏表示分类器（MtSRC）的使用。与传统的粗粒度统计图库与探针手背图像之间关联SIFT特征个数的方法相比，MtSRC精确计算图库特征重构的探针每个特征的误差，并将所有特征的误差合并进行相似性度量，达到更高的识别精度。所提出的方法进行评估的NCUT A部分数据库，并显示其有效性的识别和验证方案。此外，在NCUT Part B数据集上获得的实验结果强调了其对低质量图像的通用性和鲁棒性。
Due to the great progress made by local feature matching in both performance and robustness of dorsal hand vein recognition, this paper proposes a novel and effective approach for such an issue by improving two major steps of the SIFT-like framework,i.e.key-point detection and matching. For the former, a new key-point generation pattern, namely Centroid-based Circular Key-point Grid (CCKG), is presented, which efficiently localizes a certain number of points on the dorsal hand for the following SIFT feature extraction, leading to a discriminative description. In contrast to the existing key-point detectors, CCKG comprehensively accounts for the properties of the dorsal hand, including the vein network as well as the surrounding corium region, and hence achieves both good representativeness and low complexity. For the latter, a fine-grained matching process is introduced which makes use of Multi-task Sparse Representation Classifier (MtSRC). Compared with the traditional coarse-grained one that counts the number of associated SIFT features between the gallery and probe dorsal hand images, MtSRC precisely calculates the error of each feature of the probe as reconstructed by the gallery features, and all the errors of the probe features are combined for similarity measurement, reaching a better accuracy in recognition. The proposed approach is evaluated on the NCUT Part A database and shows its effectiveness in both the identification and verification scenarios. Additionally, the experimental results achieved on the NCUT Part B dataset highlight its generality and robustness to low quality images.
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DOI：
10.1007/978-3-642-14922-1_61
发表时间：
2010-08
期刊：
--
影响因子：
--
作者：
Yiding Wang;Kefeng Li;Jiali Cui;L. Shark;M. Varley
通讯作者：
Yiding Wang;Kefeng Li;Jiali Cui;L. Shark;M. Varley
DOI：
10.1023/b:visi.0000029664.99615.94
发表时间：
2004-11-01
影响因子：
19.5
作者：
Lowe, DG
通讯作者：
Lowe, DG
DOI：
--
发表时间：
2004
期刊：
--
影响因子：
--
作者：
通讯作者：
--
DOI：
10.1016/j.patcog.2007.07.012
发表时间：
2008-03
期刊：
Pattern Recognit.
影响因子：
--
作者：
Lingyu Wang;G. Leedham;Siu-Yeung Cho
通讯作者：
Lingyu Wang;G. Leedham;Siu-Yeung Cho
DOI：
10.1109/icip.2016.7532939
发表时间：
2016-08
期刊：
2016 IEEE International Conference on Image Processing (ICIP)
影响因子：
--
作者：
Xiaoxia Li;Di Huang;Renke Zhang;Yunhong Wang;Xianbo Xie
通讯作者：
Xiaoxia Li;Di Huang;Renke Zhang;Yunhong Wang;Xianbo Xie