In past decades, many techniques have been used to improve face recognition performance. The most common and well-studied ways are to use the whole face image to build a subspace based on the reduction of dimensionality. Differing from methods above, we consider face recognition as an image classification problem. The face images of the same person are considered to fall into the same category. Each category and each face image could be both represented by a simple pyramid histogram. Spatial dense scale-invariant feature transform features and bag of features method are used to build categories and face representations. In an effort to make the method more efficient, a linear support vector machine solver, Pegasos, is used for the classification in the kernel space with additive kernels instead of nonlinear SVMs. Our experimental results demonstrate that the proposed method can achieve very high recognition accuracy on the ORL, YALE, and FERET databases.