Supervised fractional eigenfaces

Supervised Fractional Eigenfaces (SFE) is an extension of Principal Component Analysis (PCA), which uses the fractional covariance matrix, class label information, and nonlinear data transformation to extract discriminant features. The proposed method combines techniques of two state-of-the-art feature extractors: Fractional Eigenfaces and Dual Supervised PCA. Supervised Fractional Eigenfaces was evaluated in three known face datasets and it achieved significant smaller recognition error.