Feature-Level Fusion of Finger Biometrics Based on Multi-set Canonical Correlation Analysis

Feature fusion-based multimodal biometrics has become an increasing interest to many researchers in recent years, particularly for finger biometrics. In this paper, a novel multimodal finger biometric method based on Multi-set Canonical Correlation Analysis (MCCA) is proposed. It combines finger vein, fingerprint, finger shape and finger knuckle print features of a single human finger. The proposed approach transforms multiple unimodal feature vectors into sets of canonical correlation variables, which represent fused features more efficiently in few dimensions. The experimental results on a merged multimodal finger biometric database show that the proposed approach has significant improvements over the existing approaches. It is beneficial to fuse multiple features as well as achieves lower error rates.