Linear discriminant multi-set canonical correlations analysis (LDMCCA): an efficient approach for feature fusion of finger biometrics

Abstract

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.