Holistic and Gabor-local Feature-fusion for Face Recognition using Canonical Correlation Analysis (CCA)
نویسندگان
چکیده
Abstrak – In this paper, we propose a feature fusion method based on Canonical Correlation Analysis (CCA) for combining two feature extractors to increase robustness of face recognition against pose and illumination changes. At first holistic features, eigenfaces (PCA) and Gabor phase congruency image (GPCI) features are extracted from facial images respectively and then CCA finds the transformation for each extractor dataset and maximizes the correlation between them. Experiments results on Yale face image and ORL databases have shown that the fusion of those two features exhibit better performance.
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