Finger vein recognition has drawn increasing attention as one of the most popular and promising biometrics due to its high distinguishes ability, security and non-invasive procedure. The main idea of traditional schemes is to directly extract features from finger vein images or patterns and then compare features to find the best match. However, the features extracted from images contain much redundant data, while the features extracted from patterns are greatly influenced by image segmentation methods. To tack these problems, this paper proposes a new finger vein recognition by generating code. The proposed method does not require an image segmentation algorithm, is simple to calculate and has a small amount of data. Firstly, the finger vein images were divided into blocks to calculate the mean value. Then the centrosymmetric coding is performed by using the generated eigenmatrix. The obtained codewords are concatenated as the feature codewords of the image. The similarity between vein codes is measured by the ratio of minimum Hamming distance to codeword length. Extensive experiments on two public finger vein databases verify the effectiveness of the proposed method. The results indicate that our method outperforms the state-of-theart methods and has competitive potential in performing the matching task.

中文翻译：

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通过生成代码识别手指静脉

手指静脉识别由于其高识别能力，安全性和非侵入性程序而成为最流行和最有前途的生物识别技术之一，引起了越来越多的关注。传统方案的主要思想是直接从手指静脉图像或图案中提取特征，然后比较特征以找到最佳匹配。但是，从图像中提取的特征包含大量冗余数据，而从模式中提取的特征则受到图像分割方法的极大影响。为了解决这些问题，本文提出了一种通过生成代码来进行新的手指静脉识别的方法。该方法不需要图像分割算法，计算简单，数据量少。首先，将手指静脉图像分成块以计算平均值。然后，通过使用生成的特征矩阵执行中心对称编码。所获得的码字被串联为图像的特征码字。静脉代码之间的相似性通过最小汉明距离与代码字长度的比值来衡量。在两个公共手指静脉数据库上的大量实验证明了该方法的有效性。结果表明，我们的方法优于最新方法，在执行匹配任务方面具有竞争潜力。