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

Fusing Face and Periocular biometrics using Canonical correlation analysis