Fusing Face and Periocular biometrics using Canonical correlation analysis

TL;DR

This paper introduces a novel biometric fusion method combining face and periocular features using canonical correlation analysis, enhancing recognition accuracy by integrating complementary information from both modalities.

Contribution

It proposes a new wavelet decomposed local binary pattern feature extractor and demonstrates improved biometric recognition performance over unimodal systems.

Findings

Enhanced recognition accuracy with multimodal fusion

WD-LBP features outperform traditional LBP and Gabor wavelet

Proposed method outperforms unimodal biometric systems

Abstract

This paper presents a novel face and periocular biometric fusion at feature level using canonical correlation analysis. Face recognition itself has limitations such as illumination, pose, expression, occlusion etc. Also, periocular biometrics has spectacles, head angle, hair and expression as its limitations. Unimodal biometrics cannot surmount all these limitations. The recognition accuracy can be increased by fusing dual information (face and periocular) from a single source (face image) using canonical correlation analysis (CCA). This work also proposes a new wavelet decomposed local binary pattern (WD-LBP) feature extractor which provides sufficient features for fusion. A detailed analysis on face and periocular biometrics shows that WD-LBP features are more accurate and faster than local binary pattern (LBP) and gabor wavelet. The experimental results using Muct face database reveals that the proposed multimodal biometrics performs better than the unimodal biometrics.