Efficient Facial and Facial Expression Recognition Using Canonical Correlation Analysis for Transform Domain Features Fusion and Classification

In this paper, an efficient facial and facial expression recognition algorithm employing Canonical Correlation Analysis (CCA) for features fusion and classification is presented. Multiple features are extracted, transformed to different transform domains and fused together. Two Dimensional Principal Component Analysis (2DPCA) is used to maintain only the principal features representing different faces. 2DPCA also maintains the spatial relation between adjacent pixels improving the overall recognition accuracy. CCA is being used for features fusion as well as classification. Experimental results on four different data sets showed that our algorithm outperform all most recent published state of the art techniques and reached 100 % recognition accuracy in most data sets.