Face recognition based on selection approach via Canonical Correlation Analysis feature fusion

Face matching is an active research topic in the last decade due to various applications in pattern recognition. Rather than using a single feature type, the fusion of many distinct features might decrease the error rate of facial recognition systems. This also increases the time processing and data storage. In this paper, we first employ feature fusion extracted from HOG and GIST descriptor from facial image and use Canonical Correlation Analysis (CCA) to combine into a single feature. Then, a feature selection approach based on Fisher ranking is considered to remove irrelevant and noisy features. The experiment is evaluated on three common datasets (AR, Georgia Tech and MUCT) which have been shown the improvement of the proposed approach.