为了提高分类精度,提出一种基于奇异点和隐马尔可夫模型(HMM)融合的指纹分类方法。分别对基于奇异点的指纹分类方法和基于HMM的指纹分类方法的信任度函数进行分配,利用证据理论求得两种方法联合作用下的基本可信度分配值。最后,根据纹形模式判定规则,选择具有最大支持度的目标完成指纹纹型分类。利用提出的方法在国际指纹竞赛数据库上做了测试,总的纹型辨识平均正确率可达94.5%,识别结果优于奇异点分类方法和HMM分类方法,具有一定的实用价值。

For improving classification accuracy,a novel fingerprint classification algorithm was proposed by combining the special capability of a singularity method and the Hidden Markov Model(HMM).The belief functions of the singularity classification and the HMM classification was assigned,respectively,then the combined belief function from the proposed method was determined by the Dempster-shafter(D-S).Finally, fingerprint classification was accomplished according to the classification criteria.The results show that the proposed method explores the effectiveness of singularity extraction and the capability of HMM in dealing with low-quality images in fingerprint classification.An experiment based on standard fingerprint datasets has verified that the classification accuracy reaches 94.5%,which indicates that the performance of the proposed algorithm is better than that of the singularity classification and HMM classification,respectively.