  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.
