基于Kaiser滤波及噪声抑制优化的虹膜识别

针对Gabor滤波器在数据截断时存在频谱泄露而使滤波通道边缘模糊的现象,本文利用适应性强且性能灵活可调的Kaiser函数,构造具有频率和方向选择性、且边缘清晰的Kaiser滤波通道,提取虹膜频率特征.通过对提取的特征进行幅值分段分析,发现虹膜特征存在一个"有效特征阈值"L,幅值高于L的特征能够有效识别虹膜,而幅值低于L的特征为不相关噪声.采用噪声抑制优化,对噪声特征设置"相位无效码",可以优化海明距离,提高同类虹膜的正确匹配率.实验表明:与Gabor滤波方法相比,本文基于Kaiser滤波的优化方法将虹膜的正确识别率由98.6%提高到99.9%,而且在锚误接受率(EAR)为0的情况下,具有更低的错误拒绝率(ERR).

Iris Recognition Based on Kaiser Filter and Noises Suppression Optimization

2D Kaiser filters with selective frequencies,selective orientations as well as changeable channels were constructed to extract iris features.The features were divided into several parts based on their amplitudes,and analysis of all the parts show that there is an 'effective amplitude threshold'(L)hiding in the iris features.The features with amplitudes bigger than L can achieve effective iris recognition,while those with amplitudes smaller than L are uncorrelated noises.By setting the noise features as "invalid codes",we optimized Hamming distance and improved the correct matching rate of same pairs of irises.Results show that compared with Gabor method,the optimization method improves the fight recognition rate from 98.6% up to 99.9 % and has a null fault acceptance rate with lower fault rejection rate.