In this work, a novel approach which called 1D local patterns by multi-scans(1DLPMS) for presenting the local features is proposed and its simplifications and extensions to facial image analysis are also considered. First, multi-scans are applied to capture different spatial information on the image with less computation than some traditional ways, such as Local Binary Patterns(LBP). Then, some 1D local patterns are given to encode the local features based on different coding rules. To make the proposed approach computationally simpler and easy to extend, grouped 1D local patterns by multiscans( G1DLPMS) is studied, which divides 1DLPMS into several groups and uses the co-occurrences of these groups. Performance assessment in face recognition under different challenges shows that the proposed approach is superior than traditional ones.