The identification of individuals by their finger dorsal patterns has become a very active area of research in recent years. In this paper, we present a multimodal biometric personal identification system that combines the information extracted from the finger dorsal surface image with the major and minor knuckle pattern regions. In particular, first the features are extracted from each single region by BSIF (binarized statistical image features) technique. Then, extracted information is fused at feature level. Fusion is followed by dimensionality reduction step using PCA (principal component analysis) + LDA (linear discriminant analysis) scheme in order to improve its discriminatory power. Finally, in the matching stage, the cosine Mahalanobis distance has been employed. Experiments were conducted on publicly available database for minor and major finger knuckle images, which was collected from 503 different subjects. Reported experimental results show that feature-level fusion leads to improved performance over single modality approaches, as well as over previously proposed methods in the literature.

中文翻译：

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用于个人身份验证的主要和次要背指关节模式的特征级融合

近年来，通过手指背侧模式识别个体已成为一个非常活跃的研究领域。在本文中，我们提出了一种多模态生物特征个人识别系统，该系统将从手指背表面图像中提取的信息与主要和次要关节图案区域相结合。特别地，首先通过BSIF（二值化统计图像特征）技术从每个单个区域中提取特征。然后，提取的信息在特征级别融合。融合之后是使用PCA（主成分分析）+ LDA（线性判别分析）方案的降维步骤，以提高其判别能力。最后，在匹配阶段，使用了余弦马氏距离。实验是在公开可用的数据库中进行的，这些数据库是从 503 名不同受试者收集的小指和大指关节图像。报告的实验结果表明，与单模态方法以及先前在文献中提出的方法相比，特征级融合提高了性能。