In this paper, we introduce a new feature representation method for face recognition. The proposed method, referred as kernel ICA, combines the strengths of the kernel and independent component analysis (ICA) approaches. For performing kernel ICA, we employ an algorithm developed by F. R. Bach and M. I. Jordan. This algorithm has proven successful for separating randomly mixed auditory signals, but it has never been applied on bidimensional signals such as images. We compare the performance of kernel ICA with classical algorithms such as PCA and ICA within the context of appearance-based face recognition problem using the FERET and ORL databases. Experimental results show that both kernel ICA and ICA representations are superior to representations based on PCA for recognizing faces across days and changes in expressions.