残缺指纹识别中若干关键技术的研究
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摘要
当世界步入信息时代的今天,对身份验证的需求日益迫切。因此,对生物特征识别技术的研究和应用进行得如火如茶,前景十分广阔。在众多的生物特征中,指纹识别作为最传统、最成熟的生物特征识别方式,具有唯一性和终生不变性两大明显优势,被广泛应用于身份验证和识别。人类使用指纹识别与验证身份已有数百年的历史,特别是进入19世纪之后,科学研究领域更加关注与对指纹识别技术的探索与研究,使之得到了快速的发展。指纹识别技术凭借其较高的实用性和可靠性,已经成为了目前应用最为广泛的生物识别技术。
现如今,许多科研机构都在深入研究指纹识别领域的各种关键技术方面表现得相当活跃,提出的算法在处理质量较好的指纹图像时,无论是在识别的速度还是精度方面都能够得到很好的保证。然而,对于低质量的残缺指纹,由于指纹存在污损、伤疤、断裂等情况,使得图像出现特征丢失过多和非线性形变严重的情况,识别起来存在一定的困难。但是在我国的指纹档案中,优良指纹所占比例有限,残缺指纹所占的比例不可小觑。因此,如何对残缺指纹进行准确、有效的识别,是指纹识别领域的一个亟待解决的问题。
本文主要从残缺指纹识别中的图像增强、特征提取、指纹匹配和指纹索引四个方面进行深入研究,并提出相关算法。本文工作的主要贡献和创新总结如下：
1.研究基于信息熵的指纹残缺区域修复与重建算法。
指纹图像是指纹识别过程的原始输入数据,图像质量的好坏直接影响指纹特征提取的精度,进而影响指纹识别的准确率。由于残缺指纹图像存在脊线结构紊乱、连接脊线断开、局部区域脊线连接模糊等图像质量较差的现象,会导致由于信息缺失而难以提取可靠的细节点。因此对于残缺指纹识别而言,首要问题就是对指纹图像进行增强,尤其是对残缺区域进行修复和重建。本研究在现有基于细节点信息的指纹方向场估计算法基础上,针对残缺指纹图像增强的需要,引入邻域中的方向场信息来对指纹残缺区域的方向场进行全面、精准的估计,然后基于估计出的方向场并结合先验知识来分析残缺部分的脊线与细节点分布情况,以对其进行修复与重建。另一方面,引入信息熵理论对多种修复与重建方案进行评估,从而确定残缺部分的最佳重建结果。提出的算法能够对面积较大并可能包含细节点的残缺区域进行较好的修复与重建,力求为后续的匹配与检索提供正确、有效的信息。
2.研究基于融合特征与模式熵的指纹匹配算法。
指纹匹配一般由两个主要的步骤组成：特征提取和相似性度量。首先,指纹图像的特征能否准确提取直接影响指纹识别结果的准确率。传统的指纹特征提取方法对指纹图像中信息的可靠性依赖程度较强,但是在实际的残缺指纹识别情况中,会产生提取到的单一可靠特征信息少、存在大量伪特征点等问题。其次,在提取了可靠的特征之后,指纹匹配的任务是度量两个特征点集之间的相似性,来判断指纹图像的匹配程度。传统的相似性度量算法对特征点模式的形变以及位置和方向误差比较敏感,同时也没有消除误匹配的能力。本研究提出了基于细节点和方向场特征来构造融合特征描述符的融合准则,通过多种特征信息的相互综合补充来提高特征的识别能力,从而为后续的相似性度量步骤打下基础。另一方面,提出基于模式熵的相似性度量方法,可以衡量匹配点集之间的一致性,以消除错误匹配,提高指纹匹配的准确率。
3.研究基于改进型GA-PSO算法(GA, Generitic Algorithm,遗传算法)(PSO, Particle Swarm Optimization,粒子群优化算法)的指纹匹配算法。
指纹匹配中最常用的方法就是基于点模式的匹配算法。这类方法对信息的存储要求较低、操作较为简单,对于质量较好的图像可以取得较好的匹配效果。然而,传统的基于点模式的匹配算法在进行残缺指纹匹配时会存在一些缺陷,如对局部特征点的位置要求高、匹配的准确率严重依赖于指纹图像校准的可靠性等,因此当指纹图像质量较差时,很难确保好的匹配效果。本研究提出了基于优化算法的指纹匹配算法来对点模式匹配做改进,针对指纹识别的具体情况,首先,基于优化过程中尽量使种群朝着全局最优解的方向进化的原则,对种群初始化方法做改进,用指纹预校准的结果估计出的最优解来作为部分初始化种群中的个体产生来源,既可以规定进化的大体方向,又可以保证算法搜索的随机性；其次,基于细节点和方向场信息构成的融合特征描述符,自适应的构造适应度函数,能够实现微观匹配和宏观匹配的融合,来弥补残缺指纹细节点量少、可靠性差的缺点。另一方面,提出基于GA和PSO两种优化算法的改进型GA-PSO算法,能够充分发挥两种算法的互补性,有效克服GA求解到一定范围时出现无为的冗余迭代的问题,提高解的多样性,取得较好的匹配性能。
4.研究基于BMHash (b-bit Minwise Hashing)算法的指纹索引算法。
在对大规模的数据库进行指纹识别时,由于要进行数量庞大的查询和匹配工作,因此相当耗时,大大降低了识别技术的效率和可用性。希望能够借由索引技术来尽可能快速的选择出与查询指纹最相似的候选指纹,降低所需搜索空间,提高识别效率。在理想情况下,指纹索引算法应该能够快速、准确和稳定的对指纹进行检索和匹配。但在实际操作中,由于指纹本身的千差万别,加之残缺指纹图像自身的噪声对算法的影响,致使指纹索引至今还是一个令人棘手的问题。本研究采用聚类的方法来构造指纹的索引特征,克服了直接使用细节点三元组信息作为索引特征时运算维度大的缺点,能够降低索引规模和提高索引效率。另一方面,提出基于BMHash算法计算指纹图像的索引值,将较大规模的特征向量集合映射到小规模的哈希表上,在检索时通过查表的方式缩小待识别图像的匹配范围,能够提高识别效率,实现空间与时间、速度与精度的权衡。
With human society stepping into the information era, the requirement of authentication is becoming increasingly urgent. Therefore, the research over biometric recognition is in full swing and has broad prospect. Because of its uniqueness and immutability, fingerprint recognition has become the most effective way applied in identification recognition and authentication. With high practicality and feasibility, fingerprint recognition had been developing rapidly especially since19century, and it has already became far more common and has force of law.
In recent years, many academies and industries have been making an in-depth research on the key technologies of incomplete fingerprint recognition, and the proposed algorithms perform well both in accuracy and efficiency on high quality fingerprint recognition. But it is still challenges for the low quality incomplete fingerprint recognition because of feature loss and non-linear deformation caused by stain, scar and broken on fingerprint images. Meanwhile, high quality fingerprints only occupy a small portion in fingerprint files of our country, and incomplete fingerprints occupy a certain proportion. Therefore, this problem needs to be solved urgently by pay more attentions to recognize incomplete fingerprint accurately and effectively.
This thesis has done many researches on the key technologies of incomplete fingerprint recognition, including image enhancement, feature extraction, fingerprint matching and fingerprint indexing, and makes the following contributions:
1. The algorithm of incomplete fingerprint regions reconstruction and reparation is proposed based on information entropy.
Since fingerprint image is the original input data of fingerprint recognition, the quality of image directly affects the precision of extracted feature, and then affects the recognition accuracy. Due to low quality circumstance such as mess, broken and blur of ridges in incomplete fingerprint, it is difficult to extract reliable minutiae. Therefore, there is first of all to do fingerprint enhancement for incomplete fingerprint, especially do incomplete regions reconstruction and reparation. Our research combines minutiae and orientation field to thoroughly estimate the unknown orientation field of incomplete regions. Then, previous outcome is used to analyze distribution of ridges and minutiae, and get several reconstruction schemes. Furthermore, schemes are measured by using entropy and the best is chosen. The proposed algorithm is proved to be effective in reconstructing and repairing incomplete fingerprint, and hence improves the performance of matching and indexing.
2. The algorithm of fingerprint matching is proposed based on fusion feature and pattern entropy.
Generally, fingerprint matching consists of two steps:feature extraction and similarity measurement. Firstly, feature extraction directly affects the result of recognition. Traditional methods of feature extraction depend on the reliability of information in fingerprint image to a great extent, but false features usually exist due to low quality of incomplete fingerprint image. Secondly, after feature extraction, similarity measurement is to measure the similarity of two feature sets and then to decide whether two fingerprints are similar or not. Most of the traditional methods of similarity measurement are sensitive to errors of location and orientation of feature sets, and also cannot eliminate false matches. Our research proposes a fusion feature with more comprehensive discrimination based on both the minutiae and orientation field feature. Furthermore, we use pattern entropy for similarity measure which could measure the consistency of feature sets and eliminate most of the false matches. Finally, we got a satisfactory accuracy of incomplete fingerprint matching in the experiment.
3. The algorithm of fingerprint matching based on improved GA-PSO (Generitic Algorithm-Particle Swarm Optimization).
Minutiae-based matching algorithm is most commonly applied in fingerprint matching. It has the advantages of low storage cost, simple operation and great results for high quality fingerprint. However, traditional minutiae-based matching algorithm has its deficiencies in incomplete fingerprint matching. For instance, it requires more in location of local feature sets and reliability of pre-alignment. Therefore, it is hard to ensure the matching result. Our research improves minutiae-based matching based on optimization algorithms. Firstly, population initialization is improved by using optimal solution based on pre-alignment as parts of original individuals. This could ensure both the revolution direction and the random search. Secondly, fitness function is improved by using fusion feature based on minutiae and orientation field. This could remedy the defects of few and low reliability minutiae extracted in incomplete fingerprint. Furthermore, improved GA-PSO is proposed which is a complementary scheme for solely GA or PSO. It could overcome the redundancy iteration in GA and promote the diversity of the solutions.
4. The algorithm of fingerprint indexing based on BMHash (b-bit Minwise Hashing).
Identifying an unknown fingerprint over a large-scale database still faces challenging problems in terms of both efficiency and usability. To reduce the search space and increase the recognition efficiency, fingerprint indexing is often adopted as a pre-filtering technique to select several most similar fingerprints with query fingerprint. Ideally, fingerprint indexing algorithm could do indexing and matching rapidly and steadily. But it is difficult for incomplete fingerprint indexing because of differences and noise of images. Our research constructs the indexing feature vectors from the minutiae triplets feature by clustering. It could reduce computational burden greatly. And then, BMHash is used to generate index value, which proved to be very effective in mapping mass feature vectors into a small hash table. In this way, we could make a tradeoff between space and time, speed and accuracy.
现如今,许多科研机构都在深入研究指纹识别领域的各种关键技术方面表现得相当活跃,提出的算法在处理质量较好的指纹图像时,无论是在识别的速度还是精度方面都能够得到很好的保证。然而,对于低质量的残缺指纹,由于指纹存在污损、伤疤、断裂等情况,使得图像出现特征丢失过多和非线性形变严重的情况,识别起来存在一定的困难。但是在我国的指纹档案中,优良指纹所占比例有限,残缺指纹所占的比例不可小觑。因此,如何对残缺指纹进行准确、有效的识别,是指纹识别领域的一个亟待解决的问题。
本文主要从残缺指纹识别中的图像增强、特征提取、指纹匹配和指纹索引四个方面进行深入研究,并提出相关算法。本文工作的主要贡献和创新总结如下：
1.研究基于信息熵的指纹残缺区域修复与重建算法。
指纹图像是指纹识别过程的原始输入数据,图像质量的好坏直接影响指纹特征提取的精度,进而影响指纹识别的准确率。由于残缺指纹图像存在脊线结构紊乱、连接脊线断开、局部区域脊线连接模糊等图像质量较差的现象,会导致由于信息缺失而难以提取可靠的细节点。因此对于残缺指纹识别而言,首要问题就是对指纹图像进行增强,尤其是对残缺区域进行修复和重建。本研究在现有基于细节点信息的指纹方向场估计算法基础上,针对残缺指纹图像增强的需要,引入邻域中的方向场信息来对指纹残缺区域的方向场进行全面、精准的估计,然后基于估计出的方向场并结合先验知识来分析残缺部分的脊线与细节点分布情况,以对其进行修复与重建。另一方面,引入信息熵理论对多种修复与重建方案进行评估,从而确定残缺部分的最佳重建结果。提出的算法能够对面积较大并可能包含细节点的残缺区域进行较好的修复与重建,力求为后续的匹配与检索提供正确、有效的信息。
2.研究基于融合特征与模式熵的指纹匹配算法。
指纹匹配一般由两个主要的步骤组成：特征提取和相似性度量。首先,指纹图像的特征能否准确提取直接影响指纹识别结果的准确率。传统的指纹特征提取方法对指纹图像中信息的可靠性依赖程度较强,但是在实际的残缺指纹识别情况中,会产生提取到的单一可靠特征信息少、存在大量伪特征点等问题。其次,在提取了可靠的特征之后,指纹匹配的任务是度量两个特征点集之间的相似性,来判断指纹图像的匹配程度。传统的相似性度量算法对特征点模式的形变以及位置和方向误差比较敏感,同时也没有消除误匹配的能力。本研究提出了基于细节点和方向场特征来构造融合特征描述符的融合准则,通过多种特征信息的相互综合补充来提高特征的识别能力,从而为后续的相似性度量步骤打下基础。另一方面,提出基于模式熵的相似性度量方法,可以衡量匹配点集之间的一致性,以消除错误匹配,提高指纹匹配的准确率。
3.研究基于改进型GA-PSO算法(GA, Generitic Algorithm,遗传算法)(PSO, Particle Swarm Optimization,粒子群优化算法)的指纹匹配算法。
指纹匹配中最常用的方法就是基于点模式的匹配算法。这类方法对信息的存储要求较低、操作较为简单,对于质量较好的图像可以取得较好的匹配效果。然而,传统的基于点模式的匹配算法在进行残缺指纹匹配时会存在一些缺陷,如对局部特征点的位置要求高、匹配的准确率严重依赖于指纹图像校准的可靠性等,因此当指纹图像质量较差时,很难确保好的匹配效果。本研究提出了基于优化算法的指纹匹配算法来对点模式匹配做改进,针对指纹识别的具体情况,首先,基于优化过程中尽量使种群朝着全局最优解的方向进化的原则,对种群初始化方法做改进,用指纹预校准的结果估计出的最优解来作为部分初始化种群中的个体产生来源,既可以规定进化的大体方向,又可以保证算法搜索的随机性；其次,基于细节点和方向场信息构成的融合特征描述符,自适应的构造适应度函数,能够实现微观匹配和宏观匹配的融合,来弥补残缺指纹细节点量少、可靠性差的缺点。另一方面,提出基于GA和PSO两种优化算法的改进型GA-PSO算法,能够充分发挥两种算法的互补性,有效克服GA求解到一定范围时出现无为的冗余迭代的问题,提高解的多样性,取得较好的匹配性能。
4.研究基于BMHash (b-bit Minwise Hashing)算法的指纹索引算法。
在对大规模的数据库进行指纹识别时,由于要进行数量庞大的查询和匹配工作,因此相当耗时,大大降低了识别技术的效率和可用性。希望能够借由索引技术来尽可能快速的选择出与查询指纹最相似的候选指纹,降低所需搜索空间,提高识别效率。在理想情况下,指纹索引算法应该能够快速、准确和稳定的对指纹进行检索和匹配。但在实际操作中,由于指纹本身的千差万别,加之残缺指纹图像自身的噪声对算法的影响,致使指纹索引至今还是一个令人棘手的问题。本研究采用聚类的方法来构造指纹的索引特征,克服了直接使用细节点三元组信息作为索引特征时运算维度大的缺点,能够降低索引规模和提高索引效率。另一方面,提出基于BMHash算法计算指纹图像的索引值,将较大规模的特征向量集合映射到小规模的哈希表上,在检索时通过查表的方式缩小待识别图像的匹配范围,能够提高识别效率,实现空间与时间、速度与精度的权衡。
With human society stepping into the information era, the requirement of authentication is becoming increasingly urgent. Therefore, the research over biometric recognition is in full swing and has broad prospect. Because of its uniqueness and immutability, fingerprint recognition has become the most effective way applied in identification recognition and authentication. With high practicality and feasibility, fingerprint recognition had been developing rapidly especially since19century, and it has already became far more common and has force of law.
In recent years, many academies and industries have been making an in-depth research on the key technologies of incomplete fingerprint recognition, and the proposed algorithms perform well both in accuracy and efficiency on high quality fingerprint recognition. But it is still challenges for the low quality incomplete fingerprint recognition because of feature loss and non-linear deformation caused by stain, scar and broken on fingerprint images. Meanwhile, high quality fingerprints only occupy a small portion in fingerprint files of our country, and incomplete fingerprints occupy a certain proportion. Therefore, this problem needs to be solved urgently by pay more attentions to recognize incomplete fingerprint accurately and effectively.
This thesis has done many researches on the key technologies of incomplete fingerprint recognition, including image enhancement, feature extraction, fingerprint matching and fingerprint indexing, and makes the following contributions:
1. The algorithm of incomplete fingerprint regions reconstruction and reparation is proposed based on information entropy.
Since fingerprint image is the original input data of fingerprint recognition, the quality of image directly affects the precision of extracted feature, and then affects the recognition accuracy. Due to low quality circumstance such as mess, broken and blur of ridges in incomplete fingerprint, it is difficult to extract reliable minutiae. Therefore, there is first of all to do fingerprint enhancement for incomplete fingerprint, especially do incomplete regions reconstruction and reparation. Our research combines minutiae and orientation field to thoroughly estimate the unknown orientation field of incomplete regions. Then, previous outcome is used to analyze distribution of ridges and minutiae, and get several reconstruction schemes. Furthermore, schemes are measured by using entropy and the best is chosen. The proposed algorithm is proved to be effective in reconstructing and repairing incomplete fingerprint, and hence improves the performance of matching and indexing.
2. The algorithm of fingerprint matching is proposed based on fusion feature and pattern entropy.
Generally, fingerprint matching consists of two steps:feature extraction and similarity measurement. Firstly, feature extraction directly affects the result of recognition. Traditional methods of feature extraction depend on the reliability of information in fingerprint image to a great extent, but false features usually exist due to low quality of incomplete fingerprint image. Secondly, after feature extraction, similarity measurement is to measure the similarity of two feature sets and then to decide whether two fingerprints are similar or not. Most of the traditional methods of similarity measurement are sensitive to errors of location and orientation of feature sets, and also cannot eliminate false matches. Our research proposes a fusion feature with more comprehensive discrimination based on both the minutiae and orientation field feature. Furthermore, we use pattern entropy for similarity measure which could measure the consistency of feature sets and eliminate most of the false matches. Finally, we got a satisfactory accuracy of incomplete fingerprint matching in the experiment.
3. The algorithm of fingerprint matching based on improved GA-PSO (Generitic Algorithm-Particle Swarm Optimization).
Minutiae-based matching algorithm is most commonly applied in fingerprint matching. It has the advantages of low storage cost, simple operation and great results for high quality fingerprint. However, traditional minutiae-based matching algorithm has its deficiencies in incomplete fingerprint matching. For instance, it requires more in location of local feature sets and reliability of pre-alignment. Therefore, it is hard to ensure the matching result. Our research improves minutiae-based matching based on optimization algorithms. Firstly, population initialization is improved by using optimal solution based on pre-alignment as parts of original individuals. This could ensure both the revolution direction and the random search. Secondly, fitness function is improved by using fusion feature based on minutiae and orientation field. This could remedy the defects of few and low reliability minutiae extracted in incomplete fingerprint. Furthermore, improved GA-PSO is proposed which is a complementary scheme for solely GA or PSO. It could overcome the redundancy iteration in GA and promote the diversity of the solutions.
4. The algorithm of fingerprint indexing based on BMHash (b-bit Minwise Hashing).
Identifying an unknown fingerprint over a large-scale database still faces challenging problems in terms of both efficiency and usability. To reduce the search space and increase the recognition efficiency, fingerprint indexing is often adopted as a pre-filtering technique to select several most similar fingerprints with query fingerprint. Ideally, fingerprint indexing algorithm could do indexing and matching rapidly and steadily. But it is difficult for incomplete fingerprint indexing because of differences and noise of images. Our research constructs the indexing feature vectors from the minutiae triplets feature by clustering. It could reduce computational burden greatly. And then, BMHash is used to generate index value, which proved to be very effective in mapping mass feature vectors into a small hash table. In this way, we could make a tradeoff between space and time, speed and accuracy.
引文
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