基于STM32的嵌入式指纹识别系统的设计与实现

鉴于指纹识别系统较高实时性、准确性的要求,对一种基于STM32芯片和中控光学指纹采集仪的嵌入式指纹识别系统进行了研究和实现。利用改进的Gabor滤波增强算法对指纹数据进行图像预处理后,采用指纹纹线的二值化细化中心线提取法提取图像的细节特征点,在VC++环境平台下进行仿真,结果显示系统达到了预期的效果。     

基于相似三角形原理的指纹匹配算法

为了克服指纹识别中的常见问题,提出了一种基于改进的相似三角形的指纹细节点匹配算法。该算法利用改进的Bresenham算法求得特征点间连线所穿越的脊线条数,得到模板指纹和待识别指纹特征点间的信息。     

基于中心点定位的指纹匹配算法研究

指纹识别算法是自动指纹识别系统的核心技术,基于细节匹配的算法是广泛采用的算法,但是其识别率受到中心点定位精度的限制。借鉴基于滤波的指纹识别算法的中心点识别方法,解决基于细节匹配的指纹识别算法的中心点匹配问题。对实际指纹传感器采集的指纹测试的结果表明,该指纹识别算法的识别率得到提高,具有一定的实用性。     

一种改进的基于细节点的脊线追踪算法

从点线关系来说,指纹脊线是由很多个像素点按照一定的顺序依次排列形成的线状结构。由此看来指纹脊线负载了指纹的细节特征点、普通点以及形状信息,可知指纹脊线含有大量丰富的指纹信息,几乎构成了指纹的全部特征,因此将脊线信息用于指纹识别可以提高其准确性。脊线追踪是获得指纹脊线信息的前提,本文对一种基于指纹细节端点和叉点的脊线追踪算法进行了改进,实验结果表明,改进后的脊线追踪算法对指纹细化图具有非常理想的脊线追踪效果。     

指纹网络身份认证系统中关键技术的研究

针对日益突出的互联网安全问题,介绍了指纹网络身份认证系统的功能,对身份认证系统网络拓扑结构进行了详细的研究,并结合了指纹提取方法,提出了一种基于中间点向量的指纹匹配参考点对的选择方法.在C#语言编程平台上对指纹识别算法进行了实验,实验结果表明提出的指纹识别算法能够提取指纹特征并给出指纹匹配结果.     

基于特征描述子的匹配方法

利用一种效率和匹配率均较高且存储空间小的基于特征描述子的指纹识别算法,实现了指纹匹配且可应用到嵌入式系统。由于自动指纹识别系统一般在嵌入式系统上实现,对算法的复杂度、运算速度、存储空间需要等均有较高要求,因此选用普遍使用的基于细节特征的匹配算法。该算法运算速度快,一幅300×300像素的指纹图像,仅需几十个特征向量。实验证明,算法克服了点匹配计算量大、速度慢和参考点确定难等缺点,且对图像的旋转和平移具有较好的鲁棒性。可以使基于特征描述子的随机局部区域描述子特征的匹配性能进一步提高。     

基于Tent映射混沌粒子群的快速指纹特征匹配

为了进一步提高指纹匹配算法的计算效率,本文提出了一种基于改进的Tent映射混沌粒子群优化的快速指纹特征匹配算法。首先,将粒子群优化引入基于指纹细节特征的点模式匹配中,并利用混沌的类随机性和高遍历性克服基本粒子群算法的不足。考虑到Tent映射比Logistic映射具有更好的遍历性,且基于Tent映射的混沌优化可进一步提高寻优效率,故利用改进的Tent映射混沌粒子群算法优化指纹细节特征匹配的几何变换参数估计,提高搜索过程的收敛精度和运算速度;然后,采用分层匹配的方法,设计了相应的细节特征匹配适应度函数,在粗匹配后利用具有平移旋转不变性的细节特征点的局部结构信息确定特征点对的匹配关系,以抵抗指纹图像旋转、平移和局部非线性形变等因素的影响;最后,给出了针对FVC2006指纹数据库进行的大量指纹细节特征匹配实验的结果及其客观定量评价。结果表明:与最近文献中提出的基于遗传算法的指纹特征匹配算法相比,本文提出的方法匹配精度更高,且运算速度提高了约一倍。      

一种指纹宏观曲率特征提取算法

在自动指纹识别系统中,如何有效的利用大量的指纹脊线弯曲信息是个重要问题.本文提出了一种新算法,通过综合邻近几条指纹脊线的信息,求出了一种能反映指纹宏观弯曲规律的特征.实验结果表明,该算法提取出的特征清楚地描述了指纹脊线的弯曲规律,同时对噪声不敏感,可作为指纹识别的辅助特征.     

改进的指纹识别算法嵌入式应用研究

研究了一种新的指纹识别算法并在电控锁中的应用问题.针对传统的指纹识别算法对指纹的细节匹配精确度较低,难易对细节信息进行准确的提取,为了克服这一问题,对点模式指纹识别匹配算法进行了改进,主要创新点在于改进的算法在模式匹配算法的基础上加入了匹配权重系数,有效地提高了指纹识别的精确度,同时给出了指纹识别硬件系统结构,将提出的改进的算法成功应用在嵌入式应用中,实例结果表明,提出的算法能够有效地提高安全加密度,具有一定的实际应用价值.     

一种改进的指纹细节特征提取算法

在指纹录入以及预处理中易引入噪声,因此在特征提取中,存在大量的伪特征,影响指纹的特征信息提取。本文提出了一种新的伪特征去除算法,即利用特征点为中心建立特征点检测窗口,根据一定的条件对伪特征点进行删除,另外本文提出了一种利用计算特征点到中心点的纹线数取代计算这两者之间的距离的特征提取算法,更加精确地记录了指纹的实际特征,为提高指纹识别的精度奠定了良好的基础。     

A Fingerprint Minutiae Matching Method Based on Line Segment Vector

Minutiae-based fingerprint matching is the most commonly used in an automatic fingerprint identification system. In this paper, we propose a minutia matching method based on line segment vector. This method uses all the detected minutiae （the ridge ending and the ridge bifurcation） in a fingerprint image to create a set of new vectors （line segment vector）. Using these vectors, we can determine a truer reference point more efficiently. In addition, this new minutiae vector can also increase the accuracy of the minutiae matching. By experiment on the public domain collections of fingerprint images fvc2004 DID set A and DB4 set A, the result shows that our algorithm can obtain an improved verification performance.     

Fingerprint indexing based on minutiae-dependent statistical codes

Methods based on minutiae matching have been extensively used in fingerprint recognition because minutiae can be reliably extracted from poor quality and noisy fingerprints. However, structures have to be defined due to minutiae displacements and irreproducibility. Some of the structures, though very efficient, incur large computational complexities. In this article, a feature vector of statistically based values derived from the minutiae pattern in a fingerprint is proposed for indexing fingerprints using the incremental search retrieval method. The proposed indexing technique in combination with the incremental retrieval method proves to have an added advantage over certain minutiae-based structures, especially when the minutiae points are numerous in a fingerprint. The feature vector for a fingerprint requires negligible storage resources and, consequently, the computational time in the retrieval of a candidate list for a query fingerprint is very little.     

Blurred fingerprint image enhancement: algorithm analysis and performance evaluation

A conventional automatic fingerprint matching process uses similarity score to quantify similarity between fingerprint images to be matched, and the similarity score can be determined with a minutiae extraction algorithm (MEA) which extracts minutiae from fingerprint images. The performance of MEA relies on the quality of fingerprint images. In case of blurred fingerprint images, it becomes difficult to obtain a reliable similarity score. As the result, an image enhancement algorithm should be incorporated with MEA when the fingerprint image is blurred. In this study, Volterra filter is proposed to enhance blurred fingerprints and compared against different enhancement algorithms. Experimental results show that Volterra filter outperforms other techniques such as Laplacian, Wiener, and Gabor filters for enhancing blurred images and its calculation complexity is moderate among techniques considered in this study.     

基于局部特性的指纹细节点提取算法

提出了一种基于二值指纹局部特性提取细节点的算法.该算法与快速并行细化算法相比,在提取细节点准确性相当的情况下,速度大为提升.使用FVC2002指纹数据库测试表明,该算法可靠、高效.     

Reconstructing Orientation Field From Fingerprint Minutiae to Improve Minutiae-Matching Accuracy

Minutiae are very important features for fingerprint representation, and most practical fingerprint recognition systems only store the minutiae template in the database for further usage. The conventional methods to utilize minutiae information are treating it as a point set and finding the matched points from different minutiae sets. In this paper, we propose a novel algorithm to use minutiae for fingerprint recognition, in which the fingerprint's orientation field is reconstructed from minutiae and further utilized in the matching stage to enhance the system's performance. First, we produce ldquovirtualrdquo minutiae by using interpolation in the sparse area, and then use an orientation model to reconstruct the orientation field from all ldquorealrdquo and ldquovirtualrdquo minutiae. A decision fusion scheme is used to combine the reconstructed orientation field matching with conventional minutiae-based matching. Since orientation field is an important global feature of fingerprints, the proposed method can obtain better results than conventional methods. Experimental results illustrate its effectiveness.     

基于细节点与方向场信息的指纹残缺区域重建算法

 由于指纹的残缺区域缺失有效信息,使得通过细节点和脊线信息来进行指纹匹配的工作难以进行.本文针对指纹残缺区域中脊线断裂和细节点缺失的现象,提出了一种重建算法.融合邻域中的细节点和方向场信息对残缺区域的方向场进行估计;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,对其进行修复;最后,引入信息熵对多种重建方案进行评估,从而确定残缺部分的最佳重建结果.实验结果表明,该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重建,进而提高后续匹配过程的准确率.     

指纹图像预处理和特征提取

在对现有的指纹图像增强算法研究和分析的基础上,结合指纹图像的方向特性和灰度特性,提出了一套较完整的指纹图像预处理和特征提取算法.先利用Kirsch算子得到指纹图像的方向场,并对指纹图像进行方向中值滤波,然后通过图像的边缘强度设计局部自适应阈值对指纹图像进行二值化,最后在指纹的细化图像上提取出指纹的细节特征点,并对其进行去伪处理.实验结果表明,该方法处理效果好,运算速度快.     

基于ARM和DSP的嵌入式监考系统的研究

针对考试替考现象以及考场作弊行为,提出了基于ARM和DSP双核处理器的硬件架构。对指纹细节点进行Delaunay三角剖分,利用三角形检索方法进行指纹特征值的快速查询,从而实现对考生身份的识别。采用x264开源算法处理视频图像,通过视频监控技术对考场情况进行观察。     

Fingerprint recognition by combining global structure and local cues.

As an important feature, orientation field describes the global structure of fingerprints. It provides robust discriminatory information other than traditional widely-used minutiae points. However, there are few works explicitly incorporating this information into fingerprint matching stage, partly due to the difficulty of saving the orientation field in the feature template. In this paper, we propose a novel representation for fingerprints which includes both minutiae and model-based orientation field. Then, fingerprint matching can be done by combining the decisions of the matchers based on the global structure (orientation field) and the local cue (minutiae). We have conducted a set of experiments on large-scale databases and made thorough comparisons with the state-of-the-arts. Extensive experimental results show that combining these local and global discriminative information can largely improve the performance. The proposed system is more robust and accurate than conventional minutiae-based methods, and also better than the previous works which implicitly incorporate the orientation information. In this system, the feature template takes less than 420 bytes, and the feature extraction and matching procedures can be done in about 0.30 s. We also show that the global orientation field is beneficial to the alignment of the fingerprints which are either incomplete or poor-qualitied.