l1-minimization algorithm is one of the hot topics in the signal processing and optimization communities in solving the sparsest matrix. Compared with the traditional principal component analysis using l2 norm, the l1 norm only calculates the main characteristics matrix of the image, which is more robust to noise and abnormal data. While it is used too few in wood identification. The local binary pattern (LBP) texture analysis operator is defined as a gray-scale invariant texture measure. LBP algorithm is important in view point of pattern classification, and can be used to extract three-layer cross-sectional features of different wood RGB images data. And then a fast l1 norm algorithm is used to implement fast and accurate identification to judge whether the wood surface has defects or not, and where defects locate. Many experiments indicte that fast l1 algorithm combined with LBP can get correction of 0.931 for defect location in wood surface.