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首页> 外文期刊>Multimedia Tools and Applications >Linear discriminant multi-set canonical correlations analysis (LDMCCA): an efficient approach for feature fusion of finger biometrics
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Linear discriminant multi-set canonical correlations analysis (LDMCCA): an efficient approach for feature fusion of finger biometrics

机译：线性判别多集规范相关分析（LDMCCA）：手指生物特征的有效融合方法

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摘要

Feature fusion-based multimodal biometrics has become an increasing interest to many researchers in recent years, particularly for finger biometrics. There are, however, many challenges in fusing multiple feature sets, as the case with Canonical Correlation Analysis (CCA) and Multi-set Canonical Correlation Analysis (MCCA). How to extend them to fuse multiple feature sets is a significant problem in general. In this paper, we propose a novel multimodal finger biometric method, which provides feature fusion approach called linear discriminant multi-set canonical correlation analysis (LDMCCA). It combines finger vein, fingerprint, finger shape and finger knuckle print features of a single human finger. Compared with CCA and MCCA, LDMCCA contains the class information of the training samples and represents the fused features more efficiently and discriminatively in few dimensions. The experimental results on a merged multimodal finger biometric database show that LDMCCA is beneficial to fuse multiple features as well as achieves lower error rates than the existing approaches.
机译：近年来，基于特征融合的多峰生物特征已引起许多研究人员的关注，尤其是手指生物特征。但是，在融合多个功能集方面存在许多挑战，例如典范相关分析（CCA）和多集典范相关分析（MCCA）。通常，如何扩展它们以融合多个功能集是一个重大问题。在本文中，我们提出了一种新颖的多峰手指生物特征识别方法，该方法提供了称为线性判别多集规范相关分析（LDMCCA）的特征融合方法。它结合了单个人手指的手指静脉，指纹，手指形状和手指关节打印功能。与CCA和MCCA相比，LDMCCA包含训练样本的类别信息，并在较少的维度上更有效和有区别地表示融合特征。在合并的多峰手指生物特征数据库上的实验结果表明，与现有方法相比，LDMCCA有益于融合多个功能，并且实现了更低的错误率。

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