In this paper, we present a novel dense local image representation method called Local Walsh Transform (LWT) by applying the well-known Walsh Transform (WT) to each pixel of an image. The LWT decomposes an image into multiple components, and produces LWT complex images by using the symmetrical relationship between them. Cascaded LWT (CLWT) is also a dense local image representation obtained by applying the LWT again to real and imaginary parts of LWT complex images. Applying the LWT once more to real and imaginary parts of LWT complex images increases the success rate especially on low resolution images. In order to combine the advantages of sparse and dense local image representations, we present Patch-based LWT (PLWT) and Patch based CLWT (PCLWT) by applying the LWT and CLWT, respectively, to patches extracted around landmarks of multi-scaled face images. The extracted high dimensional features of the patches are reduced through the application of the Whitened Principal Component Analysis (WPCA). Experimental results show that both the PLWT and PCLWT are robust to illumination and expression changes, occlusion and low resolution. The state-ofthe-art performance is achieved on the FERET and SCface databases, and the second best unsupervised category result is achieved on the LFW database. (C) 2017 Elsevier B.V. All rights reserved.