提出了一种可导向指纹匹配方法的分类因子，通过对不同指纹图像的分类，获得指纹图像对应最适合的匹配法。分类因子基于纹理规则度，纹理能量集中度，纹理平行度，纹理均匀度四种纹理描绘子，分别从图像全局和局部，时域和频域几个不同方面对图像进行分析；通过整合形成描绘子，用与门来实现导向匹配方法。实验结果表明，此方法不必对图像做复杂的前处理，可快速而准确地区分不同类型的图像，综合不同方法的优势，从而从整体上提高了指纹识别的正确率和匹配速度。

This paper presents a factor that could direct to different fingerprint identification methods. With this factor, different fingerprint images can be classified easily and be suited for its best recognition method. Furthermore, this factor is based on four descriptors which are called: texture regularity descriptor, texture energy centralization descriptor, texture parallelism descriptor, and texture uniformity descriptor to analysis images from both local and global, frequency and time domain Finally, these four descriptors are joined together to classify the images by AND gate. Experimental result shows that because of the method proposed in the paper, it is not necessary to do complex preprocess before recognition and it can improve the accuracy and speed of fingerprint identification.