Kang Wenxiong, Liu Yang, Wu Qiuxia, Yue Xishun
College of Automation Science and Engineering, South China University of Technology, Guangzhou, China.
Guangzhou Institute of Modern Industrial Technology, South China University of Technology, Guangzhou, China.
PLoS One. 2014 May 27;9(5):e97548. doi: 10.1371/journal.pone.0097548. eCollection 2014.
Contact-free palm-vein recognition is one of the most challenging and promising areas in hand biometrics. In view of the existing problems in contact-free palm-vein imaging, including projection transformation, uneven illumination and difficulty in extracting exact ROIs, this paper presents a novel recognition approach for contact-free palm-vein recognition that performs feature extraction and matching on all vein textures distributed over the palm surface, including finger veins and palm veins, to minimize the loss of feature information. First, a hierarchical enhancement algorithm, which combines a DOG filter and histogram equalization, is adopted to alleviate uneven illumination and to highlight vein textures. Second, RootSIFT, a more stable local invariant feature extraction method in comparison to SIFT, is adopted to overcome the projection transformation in contact-free mode. Subsequently, a novel hierarchical mismatching removal algorithm based on neighborhood searching and LBP histograms is adopted to improve the accuracy of feature matching. Finally, we rigorously evaluated the proposed approach using two different databases and obtained 0.996% and 3.112% Equal Error Rates (EERs), respectively, which demonstrate the effectiveness of the proposed approach.
非接触式掌静脉识别是手部生物识别领域中最具挑战性和前景的领域之一。鉴于非接触式掌静脉成像中存在的现有问题，包括投影变换、光照不均匀以及难以提取精确的感兴趣区域（ROI），本文提出了一种用于非接触式掌静脉识别的新颖方法，该方法对分布在手掌表面的所有静脉纹理（包括手指静脉和掌静脉）进行特征提取和匹配，以最大限度地减少特征信息的损失。首先，采用一种将高斯差分（DOG）滤波器和直方图均衡化相结合的分层增强算法，以减轻光照不均匀并突出静脉纹理。其次，采用与尺度不变特征变换（SIFT）相比更稳定的局部不变特征提取方法——RootSIFT，来克服非接触模式下的投影变换。随后，采用一种基于邻域搜索和局部二值模式（LBP）直方图的新颖分层错配去除算法，以提高特征匹配的准确性。最后，我们使用两个不同的数据库对所提出的方法进行了严格评估，分别获得了0.996%和3.112%的等错误率（EER），这证明了所提出方法的有效性。
