Gabor-based kernel PCA with doubly nonlinear mapping for face recognition with a single face image

A novel Gabor-based kernel principal component analysis (PCA) with doubly nonlinear mapping is proposed for human face recognition. The proposed algorithm is evaluated based on various databases by using different face recognition methods such as PCA, Gabor wavelets plus PCA, and Gabor wavelets plus kernel PCA with fractional power polynomial methods, where experiments show that consistent and promising results are obtained.