For robustness, many methods have been improved and presented. For handling contiguously occluded face recognition, such as disguise or expression variation, a modular weighted global sparse representation method was proposed in 13, which divided the image into modules and determined the reliability of each module based on its sparsity and residual. Next, a reconstructed image from the modules weighted by their reliability is formed for robust recognition. To obtain rotation and scale invariance, in 14, the authors constructed a dictionary based on a large number of vehicle images captured at different angles and distances, which made the dictionary large scale and the method time consuming. In 15, a practical face recognition system was presented, which gained robustness for registration and illumination by minimizing the sparsity of the registration error and capturing a sufficient set of training illuminations for linearly interpolating practical lighting conditions, respectively. In 16, the authors presented a block-based face-recognition algorithm, which is based on a sparse linear-regression subspace model via a locally adaptive dictionary constructed from the past observable data (i.e., training samples). Though it obtained a high recognition rate, prealignment and a certain scale were always required, i.e., those methods are more suitable for applications in constrained environments. To handle the problem of alignment, in 17 the authors introduced SIFT descriptors18 to the SRC framework, and proposed multikeypoint descriptors SRC (MKD-SRC) method, which has achieved preliminary success on both holistic and partial face recognition. Additionally, modified MKD-SRC has been proposed based on the Gabor Ternary pattern (GTP) descriptors in 19. Those two methods may be affiliated to a feature-based SRC method, which has shown good robustness for alignment and affine transform and thus may extend the application of SRC. Obviously, a feature-based dictionary is the core, and it may contain considerable useful information for recognition, which may be omitted with present methods.