基于小波包分析的虹膜识别研究  

Research on Iris Recognition Algorithm Based on Wavelet Packet Decomposition

作　　者：周俊 王帅 刘凡漪 ZHOU Jun;WANG Shuai;LIU Fan-yi(Department of Information Engineering,Army Service College,Chongqing 401331,China;Chongqing Modern Service Industry Research Center,Chongqing 401331,China;Basic Laboratory Center,Army Service College,Chongqing 401331,China;Chongqing Jiaotong University,Chongqing 400074,China)

机构地区：[1]中国人民解放军陆军勤务学院信息工程系,重庆401331 [2]重庆现代服务业研究中心,重庆401331 [3]中国人民解放军陆军勤务学院基础部基础实验中心,重庆401331 [4]重庆交通大学,重庆400074

出　　处：《计算机科学》2021年第S01期57-62,共6页Computer Science

基　　金：重庆市教育委员会科学技术研究计划(KJZD-K202004401,KJZD-K201904401);重庆商务职业学院人工智能技术应用协同创新中心资助项目。

摘　　要：虹膜特征提取是虹膜识别中的关键环节。小波方法在提取虹膜特征时未对分解后的高频空间进一步细化分解,而虹膜纹理特征较多地蕴含在高频空间中,因此提取的虹膜特征在表示特征能力上存在不足。针对此类问题,提出一种基于小波包多尺度分解的虹膜识别方法,利用阈值将小波包分解后第二层对角高频子带图调制为虹膜特征码,利用海明距离对特征进行识别。对108类人眼虹膜图像进行特征提取与匹配,分解小波采用sym2小波,共进行5350次特征匹配,正确识别率达到98.5%,在识别性能上优于Boles的小波变换过零点法和Lim的二维Haar小波变换法,仅次于Daugman的二维Gabor方法。Iris feature extraction is the key step in iris recognition.The wavelet method does not further decompose the high-frequency space when decomposing the iris image,but the iris features are more contained in the high-frequency space,and the extracted iris features are insufficient in the feature expression capabilities.Aiming at the above problems,an iris feature recognition method based on wavelet packet multi-scale decomposition is proposed in this paper,diagonal high-frequency subband map from the second layer based on wavelet packet de-composition is modulated into iris feature code,and the feature is recognized through hamming distance.In the experiment,sym2 wavelet is used as decomposition wavelet function,which carries out 5350 times of feature matching.The results show that the correct recognition rate is 98.5%,which is superior to the wavelet zero crossing method of boles and the two-dimensional Haar wavelet transform method of Lim,is second only to the two-dimensional Gabor method of Daugman.

关 键 词：小波包 分解 对角高频 阈值化 虹膜特征 

分 类 号：TP391.5[自动化与计算机技术—计算机应用技术]