Principal Component A nalysis and Fisher Linear Discriminant methods have demonstrated their success in fac edete ction, r ecognition and tr acking. The representations in these subspace methods are based on second order statistics of the image set, and do not address higher order statistical dependencies such as the relationships among three or more pixels. Recently Higher Order Statistics and Independent Component Analysis (ICA) have been used as informative representations for visual recognition. In this paper, we investigate the use of Kernel Principal Component Analysis and Kernel Fisher Linear Discriminant for learning low dimensional representations for face recognition, which we call Kernel Eigenface and Kernel Fisherface methods.While Eigenface and Fisherface methods aim to find projection directions based on second order correlation of samples, Kernel Eigenface and Kernel Fisherface methods provide generalizations which take higher order correlations into account. We compare the performance of kernel methods with classical algorithms such as Eigenface, Fisherface, ICA, and Support Vector Machine (SVM) within the context of appearance-based face recognition problem using two data sets where images vary in pose, scale, lighting and expression. Experimental results show that kernel methods provide better representations and achieve lower error rates for face recognition.