Fusing Face and Periocular biometrics using Canonical correlation analysis
This work addresses biometric recognition challenges for security applications, but it is incremental as it builds on existing fusion methods with a new feature extractor.
The paper tackles the limitations of unimodal biometrics like face and periocular recognition by fusing them at the feature level using canonical correlation analysis, resulting in improved recognition accuracy as demonstrated on the Muct face database.
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.