指纹图像的质量测量与评价,在指纹图像分割、增强及指纹匹配等环节都有重要应用. 同时,指纹图像的质量分类,对指纹识别算法的适用性研究也有重要意义. 本文提出一种基于支持向量机的指纹图像质量分类方法.该方法选择梯度、Gabor特征、方向对比度等指标,利用支持向量机有效实现指纹图像质量分类. 并采用少类样本合成过采样技术( SMOTE)降低指纹图像质量好坏的类别不平衡问题对分类的影响. 理论分析和实验结果都表明该方法能够较为有效地提高指纹图像质量分类的正确率.-Fingerprint Image Quality Measurement and evaluation, in the fingerprint image segmentation, enhancement and fingerprint matching has important applications such links. Meanwhile, the quality of fingerprint image classification, on the applicability of fingerprint recognition algorithm is also important. In this paper, based on support vector machine classification of fingerprint image quality. The method chosen gradient, Gabor features, such as indicators of the direction of contrast, the effective use of support vector machine classification of fingerprint image quality. and use a small sample of synthetic over-sampling technique class (SMOTE) lower quality fingerprint image the type of imbalance bad influence on the classification. Theoretical analysis and experimental results show that the method can be used effectively to improve the quality of fingerprint image classification accuracy.