We report the results of a comparative study on subspace analysis methods for face recognition. In particular, we have studied four different subspace representations and their 'kernelized' versions if available. They include both unsupervised methods such as principal component analysis (PCA) and independent component analysis (ICA), and supervised methods such as Fisher discriminant analysis (FDA) and probabilistic PCA (PPCA) used in a discriminative manner. The 'kernelized' versions of these methods provide subspaces of high-dimensional feature spaces induced by non-linear mappings. To test the effectiveness of these subspace representations, we experiment on two databases with three typical variations of face images, i.e., pose, illumination and facial expression changes. The comparison of these methods applied to different variations in face images offers a comprehensive view of all the subspace methods currently used in face recognition.