虹膜纹理特征分析及其识别算法评价
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摘要
近年来,人们对个人身份的鉴定的准确性与可靠性的需求变的更高,基于生物特征识别的身份鉴别方法逐渐受到了广泛的关注,变的更加重要。虹膜是人眼的彩色环状组织,具有唯一性、稳定性和易采集性,与人脸、指纹等生物识别技术相比,虹膜识别具有更高的可靠性和稳定性,这使它成为了生物识别领域的研究热点,必将成为另一种主流的生物识别技术。
虹膜识别系统可分成四个部分:图像采集系统,预处理模块,特征提取模块和分类识别模块。本文对每个模块的作用都做了简单的介绍,并对虹膜识别的算法作了综合性的评价。虹膜包含有丰富的纹理信息,从不同的角度对纹理特征进行分析能得到不同的虹膜识别方法,这也是本文研究的重点,在综述了前人的研究成果的基础上,本文重点探讨了虹膜的纹理特征分析的方法和分类器的设计。论文的主要工作如下:
1.采用提升整数小波变换对虹膜的纹理的细节特征进行了分析和提取。与传统基于卷积的小波变换算法相比,提升整数小波计算简单,运算速度快,占用更少的内存,而且实现的是从整数到整数的变换,对虹膜纹理信息的提取量化有一定的益处,能更好的表达虹膜纹理信息,从而提高识别率。
2.图像的小波分解后的各子带包含有图像不同方向不同频率的输出信息,针对虹膜纹理的特点,我们将虹膜的低频子带看作一幅新的纹理图像进而采用Log-Gabor滤波器来进行纹理特征的提取并量化,采用计算汉明距离的方法来进行分类,实验结果表明,本算法比单纯小波变换的识别算法要有更好的识别效果。
3.汉明距是一种简单、有效、常用的分类器,但有时并不能得到最优的结果,支持向量机作为一种新兴的统计分类方法,其理论和应用正处于飞速的发展期,而且支持向量机主要是针对小样本数据集的情况,且具有良好的泛化能力,因此本文也将支持向量机应用在虹膜分类中,并取得了好的识别效果。
4.针对当前算法评价过于片面性,采用了更全面的指标体系,对虹膜的典型识别算法进行了综合的评价。
There has been a rapid increase in the need of accurate and reliable personal identification infrastructure in recent years. Biometrics has become an important technology for security. Iris is the colored part round the pupil of the eye, which is unique, stable, and inoffensive and can be collected easily. Iris recognition is an identification method based on texture features of the human eye iris to determine the identity, it is one of the most accurate biological recognition methods, and it has been applied in the security domains such as identity authentication. Compared with other biological specificity such as face and fingerprint, iris patterns are more stable and reliable. Furthermore, iris recognition system is non-invasive to the users. So the iris recognition technology has become the research focus in the current biological recognition region.
Iris recognition system can be divided into four modules: image acquisition, preprocessing, feature extraction and classification module. In this paper, each module has been simply introduced, and then a simple evaluation was done in several ris recognition algorithms. The key issues in iris recognition system are the analysis of texture features and classification, which are also focused in our research. After an overview of previous research, the paper investigates the extraction algorithms for iris texture and classifier design. The main work as follows:
1. An iris recognition algorithm based on lifting integer wavelet transformation is presented. Compared with the traditional wavelet transformation which was based on convolution, the algorinthm is simple in the operation and use the less memory, then the operation speed is improved and the transformation from integer to integer can be realized, which is beneficial to quantify the iris information.
2. Each sub-band of wavelet decomposition contains plentiful information steming from different directions and frequencies of original image. According to the characteristics of the iris image, we regard low-frequency sub-band as a new iris image, and then use the Log-Gabor filter to extract features of new image. The hamming distance is employed to carry on iris recognition. The experiment results show the algorithm has the better effect than the algorithm only using the wavelet transform.
3. Hamming distance is a simple, effective and popular classfier, but sometimes it dosen’t get the optimal results. Support vector machines (SVM) are a new method of statistical classification, and the relative theory and application are rapidly developing. SVM can effectively deal with data sets of less samples, which has the better generalization ability. Therefore, SVM is employed as a classifier in this paper, the effectiveness of which has been shown in our experimental works.
4. As the current evaluation methods for iris recognition are too unilateral, more comprehensive indices system is introduced in our research. Some typical algorithms of iris recognition have been evaluated by using these indices.
虹膜识别系统可分成四个部分:图像采集系统,预处理模块,特征提取模块和分类识别模块。本文对每个模块的作用都做了简单的介绍,并对虹膜识别的算法作了综合性的评价。虹膜包含有丰富的纹理信息,从不同的角度对纹理特征进行分析能得到不同的虹膜识别方法,这也是本文研究的重点,在综述了前人的研究成果的基础上,本文重点探讨了虹膜的纹理特征分析的方法和分类器的设计。论文的主要工作如下:
1.采用提升整数小波变换对虹膜的纹理的细节特征进行了分析和提取。与传统基于卷积的小波变换算法相比,提升整数小波计算简单,运算速度快,占用更少的内存,而且实现的是从整数到整数的变换,对虹膜纹理信息的提取量化有一定的益处,能更好的表达虹膜纹理信息,从而提高识别率。
2.图像的小波分解后的各子带包含有图像不同方向不同频率的输出信息,针对虹膜纹理的特点,我们将虹膜的低频子带看作一幅新的纹理图像进而采用Log-Gabor滤波器来进行纹理特征的提取并量化,采用计算汉明距离的方法来进行分类,实验结果表明,本算法比单纯小波变换的识别算法要有更好的识别效果。
3.汉明距是一种简单、有效、常用的分类器,但有时并不能得到最优的结果,支持向量机作为一种新兴的统计分类方法,其理论和应用正处于飞速的发展期,而且支持向量机主要是针对小样本数据集的情况,且具有良好的泛化能力,因此本文也将支持向量机应用在虹膜分类中,并取得了好的识别效果。
4.针对当前算法评价过于片面性,采用了更全面的指标体系,对虹膜的典型识别算法进行了综合的评价。
There has been a rapid increase in the need of accurate and reliable personal identification infrastructure in recent years. Biometrics has become an important technology for security. Iris is the colored part round the pupil of the eye, which is unique, stable, and inoffensive and can be collected easily. Iris recognition is an identification method based on texture features of the human eye iris to determine the identity, it is one of the most accurate biological recognition methods, and it has been applied in the security domains such as identity authentication. Compared with other biological specificity such as face and fingerprint, iris patterns are more stable and reliable. Furthermore, iris recognition system is non-invasive to the users. So the iris recognition technology has become the research focus in the current biological recognition region.
Iris recognition system can be divided into four modules: image acquisition, preprocessing, feature extraction and classification module. In this paper, each module has been simply introduced, and then a simple evaluation was done in several ris recognition algorithms. The key issues in iris recognition system are the analysis of texture features and classification, which are also focused in our research. After an overview of previous research, the paper investigates the extraction algorithms for iris texture and classifier design. The main work as follows:
1. An iris recognition algorithm based on lifting integer wavelet transformation is presented. Compared with the traditional wavelet transformation which was based on convolution, the algorinthm is simple in the operation and use the less memory, then the operation speed is improved and the transformation from integer to integer can be realized, which is beneficial to quantify the iris information.
2. Each sub-band of wavelet decomposition contains plentiful information steming from different directions and frequencies of original image. According to the characteristics of the iris image, we regard low-frequency sub-band as a new iris image, and then use the Log-Gabor filter to extract features of new image. The hamming distance is employed to carry on iris recognition. The experiment results show the algorithm has the better effect than the algorithm only using the wavelet transform.
3. Hamming distance is a simple, effective and popular classfier, but sometimes it dosen’t get the optimal results. Support vector machines (SVM) are a new method of statistical classification, and the relative theory and application are rapidly developing. SVM can effectively deal with data sets of less samples, which has the better generalization ability. Therefore, SVM is employed as a classifier in this paper, the effectiveness of which has been shown in our experimental works.
4. As the current evaluation methods for iris recognition are too unilateral, more comprehensive indices system is introduced in our research. Some typical algorithms of iris recognition have been evaluated by using these indices.
引文
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2.何杰.潜力无限的虹膜识别技术[EB/OL]. http://www.chinarfid.com.cn/JSZL/8072.htm
3. Daugman J G.High Confidence Visual Recognition of Person by a Test of Statistical Independence[J].IEEE Trans. on PAMI,1993,15(11):1148-1161
4. Daugman J G.Demodulation by Complex-valued Wavelets for Stochastic Pattern Recogniton[J].International Journal of Wavelets,Multi-resolution and Information Processing,2003,1(1):1-17
5. Daugman J G.How Iris Recognition Works[J].IEEE Trans. Circuits and Syst for Video Tech, 2004,14(1):21-30
6. Raul Sanchez-Reillo,Carmen Sanchez-Avila.Iris Recognition with Low Template Size[C].Proc of International Conference on Audio and Video-Based Biometric Person Authentication,2001.324-329
7. Carmen Sanchez-Avila,Raul Sanchez-Reillo.Iris-based Biometric Recognition Using Wavelet Transform[J].IEEE Aerospace and Electronic Systems Magzine,2002.3-6
8. Wildes R,Asmuth J,Hsu s,et al.A Machine-vision System for Iris Recognition[J].1996,6:1-8
9. Wildes R.Iris Recogniton: An Emerging Biometric Technology[J].Proceedings of the IEEE,1997,85:1348-1363
10.王蕴红,谭铁牛,朱勇.基于虹膜识别的身份鉴别[J].自动化学报,2002,28(1):1-10
11. Ma Li,Wang Yunhong,Tan Tieniu.Iris Reconition Based on Multichannel Gabor Filtering[C].proc of the Fifth Asian Conference on Computer Vision,2002.279-283
12. Ma Li,Wang Yunhong,Zhang Dexin.Efficient Iris Recognition by Characterizing Key Local Variations[J].IEEE Transactions on Image Processing,2004,13(6):739-750
13. Lim S,Lee K,Byeon O,et al.Efficient Iris Recogniton through Improvement of Feature Vector and Classifier[J].ETRI Journal,2001,23(2):61-70
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