用于身份鉴别的虹膜识别算法研究
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摘要
生物特征识别技术是利用人类自身的生理或行为特征进行身份认定的一种技术。作为新兴的生物特征识别技术,虹膜识别技术具有唯一性、稳定性、非侵犯性和天然防伪性的生理方面优势,使其在正确识别率、错误率等方面的性能指标都优于其他生物特征识别技术,是目前最有发展前途的生物特征识别技术之一,得到了国际上的广泛关注,有着广泛的市场前景和科学研究价值。
针对虹膜识别算法中存在的问题,本文在虹膜定位算法、特征提取和编码算法以及匹配法方面进行了研究,主要的贡献和创新性工作有:
(1)针对虹膜定位易受噪声影响、速度较慢、自适应性不好等问题,提出了基于迭代圆环像素率法的快速虹膜定位算法。从切割虹膜图像、对图像抽样、迭代圆环像素率法、快速的Hough圆检测以及分层定位思想5方面提高算法的速度;提出了消除瞳孔光斑的形态学方法;瞳孔分割阈值以及小范围的圆心、半径候选集等参数都是通过计算得到,自适应性好。使用了4个虹膜数据库进行实验,实验结果表明算法的定位准确率在97.75%～99.07%之间,定位时间在52.847～158.502 ms之间。该算法是一种鲁棒、快速、自适应的虹膜定位算法,其综合性能好。
(2)对传统的最佳位移Hamming距离匹配方法进行了研究,提出了改进的移位Hamming距离标准差虹膜匹配方法。首先对基于Gabor滤波器的虹膜识别方法进行了研究,构造了新的16通道奇对称Gabor滤波器组对虹膜的纹理进行不同尺度和不同方向的特征提取;然后对均匀抽样点的滤波结果进行过零检测编码;最后构造了改进的移位Hamming距离标准差参数进行匹配。实验结果表明,相对于传统匹配方法,该匹配方法的正确识别率在含噪完整虹膜库中提高0.129%,达到99.902%,在少噪缩减虹膜库中提高0.165%,达到99.949%。实验证明本文提出了一种好的基于Gabor滤波器的虹膜识别算法,本文的匹配算法是一种更好的虹膜编码匹配方法。
(3)针对虹膜识别中抗噪声能力不好、识别速度较慢、安全性不高、线性分类器的阈值选择范围小等问题,提出了一种基于LBP的虹膜识别新方法。该方法使用LBP16,4算子来提取虹膜的纹理特征,并构造了改进的移位均值标准差参数进行匹配。实验结果表明,该算法无需噪声模板的屏蔽就能达到99.976%的正确识别率;特征提取和匹配仅需59.902 ms;识别结果更倾向于拒绝而非接受,安全性高;匹配的阈值选择范围大。该算法正确识别率高、抗噪声能力好、识别速度快、安全性高、分类器的阈值选择范围大,且思想简单,易于实现。
(4)针对虹膜的LBP特征提取方法存储空间开销大的问题,提出了基于高斯金字塔的虹膜LBP特征约减方法。通过高斯金字塔的原图像压缩以及二值化的特征约减两步,将虹膜LBP特征的存储空间压缩了32倍,还提高了特征提取与匹配速度,是一种非常有效的虹膜LBP特征约减方法。实验结果表明该算法无需噪声模板的屏蔽就能达到99.968%的正确识别率,特征提取和匹配时间只有16.496 ms,是特征约减前的27%左右。该算法保持了正确识别率高、抗噪声能力好、识别速度快、安全性高的优点。
Biometrics refers to a personal identification technology based on the physiological or behavioral characteristics of human beings. As the novel biometrics, iris recognition technology has the physiological advantages of unique, stable, non-intrusive and anti-counterfeit features, so the performance criterions of correct recognition rate and error rate are better than other biometrics. Iris recognition technology is one of the best prospect biometrics, which gets broad international attentions with extensive markets and scientific study values.
Aimed at the problems of iris recognition algorithms, in this paper the algorithms of iris localization, iris feature extracting and encoding and iris matching are researched. The main contributions of the work in this thesis are as follow:
(1) Aimed at the problems of noise influence, higher time consumption and bad adaptive performance for iris localization, a rapid iris localization algorithm based on the method of iterative pixel ratio of cirque area is proposed. The 5 methods of cutting iris image, image sampling, iterative pixel ratio of cirque area, rapid circle detection of Hough transformation and layered localization theory are used to improve the algorithm speed. The morphological method is used to eliminate the pupil faculae. The parameters, such as the threshold of pupil segmentation, the small ranges of circle center and radius, and so on, are all gotten by computation with good adaptive performance. Four iris databases are applied in experiments. The experimental results show that the accuracy of the proposed algorithm is 97.75%～99.07% and the time consumption of that is 52.847～158.502 ms. It is a robust, rapid, adaptive iris localization algorithm with good comprehensive performance.
(2) Traditional iris matching method based on Hamming distance of optimal offset is researched, and the iris matching method based on improved standard deviation of offset Hamming distances is proposed. At first, the iris feature extracting method based on Gabor filter is researched, and the new 16-channel Gabor filters with odd symmetry are gotten to extract iris texture features of different scales and different directions. Then the filter results of sample points are encoded by detecting if zero-crossing. At last, the parameter of improved standard deviation of offset Hamming distances is constructed for iris matching. The experimental results show that compared with traditional matching method, the correct recognition rate of the proposed matching method is higher 0.129% reaching 99.902% in database with noises and higher 0.165% reaching 99.949% in database with little noises. The experiments demonstrate that a good iris recognition method based on Gabor filter is proposed, and the proposed matching algorithm is a better matching method of iris code.
(3) Aimed at the problems of bad anti-noise ability, lower recognition speed, lower security and small optional threshold range of linearity classifier for iris recognition, a new iris recognition method based on LBP operator is proposed. The LBP16,4 operator is used to extract iris texture features, and the parameter of improved standard deviation of offset means is constructed for iris matching. The experimental results show that the correct recognition rate of the proposed method can reach 99.976% without noise mask. The time consumption of feature extracting and matching is only 59.902 ms. The recognition result tends to rejection but not acceptance with high security and the threshold range of classifier is large. The proposed method has the advantages of high correct recognition rate, strong anti-noise ability, rapid recognition speed, high security, and large optional threshold range of classifier. The idea is simple and the method is easy to realize.
(4) Aimed at the problem of the high storage consumption of the iris feature extracting method based on LBP operator, a compressing algorithm for iris LBP features based on Gaussian pyramid is proposed. The two steps of the compressing algorithms for original image by Gaussian pyramid and for LBP feature image by thresholding save the storage space of iris LBP features 32 times. Furthermore, the proposed method improves the speed of feature extracting and matching. It is a very valid compressing method for iris LBP features. The experimental results show that the correct recognition rate of the proposed method can reach 99.968% without noise mask. The time consumption of feature extracting and matching is only 16.496 ms, and is about 27% of that before feature compressing. It holds the advantages of high correct recognition rate, strong anti-noise ability, rapid recognition speed, and high security.
针对虹膜识别算法中存在的问题,本文在虹膜定位算法、特征提取和编码算法以及匹配法方面进行了研究,主要的贡献和创新性工作有:
(1)针对虹膜定位易受噪声影响、速度较慢、自适应性不好等问题,提出了基于迭代圆环像素率法的快速虹膜定位算法。从切割虹膜图像、对图像抽样、迭代圆环像素率法、快速的Hough圆检测以及分层定位思想5方面提高算法的速度;提出了消除瞳孔光斑的形态学方法;瞳孔分割阈值以及小范围的圆心、半径候选集等参数都是通过计算得到,自适应性好。使用了4个虹膜数据库进行实验,实验结果表明算法的定位准确率在97.75%～99.07%之间,定位时间在52.847～158.502 ms之间。该算法是一种鲁棒、快速、自适应的虹膜定位算法,其综合性能好。
(2)对传统的最佳位移Hamming距离匹配方法进行了研究,提出了改进的移位Hamming距离标准差虹膜匹配方法。首先对基于Gabor滤波器的虹膜识别方法进行了研究,构造了新的16通道奇对称Gabor滤波器组对虹膜的纹理进行不同尺度和不同方向的特征提取;然后对均匀抽样点的滤波结果进行过零检测编码;最后构造了改进的移位Hamming距离标准差参数进行匹配。实验结果表明,相对于传统匹配方法,该匹配方法的正确识别率在含噪完整虹膜库中提高0.129%,达到99.902%,在少噪缩减虹膜库中提高0.165%,达到99.949%。实验证明本文提出了一种好的基于Gabor滤波器的虹膜识别算法,本文的匹配算法是一种更好的虹膜编码匹配方法。
(3)针对虹膜识别中抗噪声能力不好、识别速度较慢、安全性不高、线性分类器的阈值选择范围小等问题,提出了一种基于LBP的虹膜识别新方法。该方法使用LBP16,4算子来提取虹膜的纹理特征,并构造了改进的移位均值标准差参数进行匹配。实验结果表明,该算法无需噪声模板的屏蔽就能达到99.976%的正确识别率;特征提取和匹配仅需59.902 ms;识别结果更倾向于拒绝而非接受,安全性高;匹配的阈值选择范围大。该算法正确识别率高、抗噪声能力好、识别速度快、安全性高、分类器的阈值选择范围大,且思想简单,易于实现。
(4)针对虹膜的LBP特征提取方法存储空间开销大的问题,提出了基于高斯金字塔的虹膜LBP特征约减方法。通过高斯金字塔的原图像压缩以及二值化的特征约减两步,将虹膜LBP特征的存储空间压缩了32倍,还提高了特征提取与匹配速度,是一种非常有效的虹膜LBP特征约减方法。实验结果表明该算法无需噪声模板的屏蔽就能达到99.968%的正确识别率,特征提取和匹配时间只有16.496 ms,是特征约减前的27%左右。该算法保持了正确识别率高、抗噪声能力好、识别速度快、安全性高的优点。
Biometrics refers to a personal identification technology based on the physiological or behavioral characteristics of human beings. As the novel biometrics, iris recognition technology has the physiological advantages of unique, stable, non-intrusive and anti-counterfeit features, so the performance criterions of correct recognition rate and error rate are better than other biometrics. Iris recognition technology is one of the best prospect biometrics, which gets broad international attentions with extensive markets and scientific study values.
Aimed at the problems of iris recognition algorithms, in this paper the algorithms of iris localization, iris feature extracting and encoding and iris matching are researched. The main contributions of the work in this thesis are as follow:
(1) Aimed at the problems of noise influence, higher time consumption and bad adaptive performance for iris localization, a rapid iris localization algorithm based on the method of iterative pixel ratio of cirque area is proposed. The 5 methods of cutting iris image, image sampling, iterative pixel ratio of cirque area, rapid circle detection of Hough transformation and layered localization theory are used to improve the algorithm speed. The morphological method is used to eliminate the pupil faculae. The parameters, such as the threshold of pupil segmentation, the small ranges of circle center and radius, and so on, are all gotten by computation with good adaptive performance. Four iris databases are applied in experiments. The experimental results show that the accuracy of the proposed algorithm is 97.75%～99.07% and the time consumption of that is 52.847～158.502 ms. It is a robust, rapid, adaptive iris localization algorithm with good comprehensive performance.
(2) Traditional iris matching method based on Hamming distance of optimal offset is researched, and the iris matching method based on improved standard deviation of offset Hamming distances is proposed. At first, the iris feature extracting method based on Gabor filter is researched, and the new 16-channel Gabor filters with odd symmetry are gotten to extract iris texture features of different scales and different directions. Then the filter results of sample points are encoded by detecting if zero-crossing. At last, the parameter of improved standard deviation of offset Hamming distances is constructed for iris matching. The experimental results show that compared with traditional matching method, the correct recognition rate of the proposed matching method is higher 0.129% reaching 99.902% in database with noises and higher 0.165% reaching 99.949% in database with little noises. The experiments demonstrate that a good iris recognition method based on Gabor filter is proposed, and the proposed matching algorithm is a better matching method of iris code.
(3) Aimed at the problems of bad anti-noise ability, lower recognition speed, lower security and small optional threshold range of linearity classifier for iris recognition, a new iris recognition method based on LBP operator is proposed. The LBP16,4 operator is used to extract iris texture features, and the parameter of improved standard deviation of offset means is constructed for iris matching. The experimental results show that the correct recognition rate of the proposed method can reach 99.976% without noise mask. The time consumption of feature extracting and matching is only 59.902 ms. The recognition result tends to rejection but not acceptance with high security and the threshold range of classifier is large. The proposed method has the advantages of high correct recognition rate, strong anti-noise ability, rapid recognition speed, high security, and large optional threshold range of classifier. The idea is simple and the method is easy to realize.
(4) Aimed at the problem of the high storage consumption of the iris feature extracting method based on LBP operator, a compressing algorithm for iris LBP features based on Gaussian pyramid is proposed. The two steps of the compressing algorithms for original image by Gaussian pyramid and for LBP feature image by thresholding save the storage space of iris LBP features 32 times. Furthermore, the proposed method improves the speed of feature extracting and matching. It is a very valid compressing method for iris LBP features. The experimental results show that the correct recognition rate of the proposed method can reach 99.968% without noise mask. The time consumption of feature extracting and matching is only 16.496 ms, and is about 27% of that before feature compressing. It holds the advantages of high correct recognition rate, strong anti-noise ability, rapid recognition speed, and high security.
引文
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