As the Center-Symmetric Local Binary Pattern(CS-LBP) for the iris recognition has a higher feature dimension and is sensitive to noises, an effective improved method based on Statistical Characteristics Center-symmetric Local Binary Pattern(SCCS-LBP) was proposed. Firstly, a normalized iris image was encoded by CS-LBP according to the distribution characteristics of iris texture, and the statistical characteristics of the encoded image was computed to reduce the feature dimension. Then, the binary feature image of iris was extracted based on statistical results. Finally, the Hamming distance matching vector was obtained to implement the iris recognition. This method was used to CASIA1.0,CASIA2.0,CASIA3.0-Interval and MMU1 database, the results show that the highest correct recognition rates reach respectively 99.955%, 99.848%,99.989%, and 99.916%. The experimental results demonstrate that this method effectively utilizes the iris texture distribution characteristics, and have the advantages of lower dimension, higher recognition rate and better robustness as compared with LBP and CS-LBP methods.