指横纹识别系统及关键技术的研究与实现
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摘要
信息时代的飞速发展加剧了信息安全的重要性,生物特征识别作为身份验证的重要手段,一直是信息安全的主流研究方向。由于生物特征识别技术利用人体固有的生理特征或行为作为信息,故而在身份验证的安全性上体现出其他技术无法比拟的优越性,但是传统的生物识别技术在实际应用上都存在一定缺点。本文研究并开发了一款新的生物特征识别系统,在利用指横纹来克服传统生物识别技术存在的缺点的同时,开辟一种新颖的身份识别手段,为生物识别提供新的借鉴。
本文首先探讨了指横纹的特点,分析了指横纹识别系统的原理、功能需求、性能指标,给出指横纹识别系统的完整设计方案,并对指横纹识别系统及关键技术进行详细的归纳总结。
其次,在手指分割部分：针对手指基准点难于定位的问题,结合最小特征根分析的方法利用曲线的非连续性特征,首先选定手掌轮廓线上的一个像素点和包含此点的一段连续轮廓线作为分析对象,再采用了一个宏观的共线性判定指标判断此段轮廓是不是构成了手指基准点所在的曲线,接着通过这些像素点坐标统计构成的协方差矩阵的最小特征根的值来定位手指基准点区域,最后采用拐点分析法矫正奇异区域得到手指基准点。解决了传统方法依靠求取曲率和低通滤波得到近似手指基准点而存在的耗时大、复杂性高和不精确的问题。同时利用最小二乘法直线拟合技术,拟合四指外轮廓的方法分割单指,该方法利用人体四指外轮廓类似直线的特性,很好的区分各个手指,有效实现单指循环分割识别。
第三,在指横纹分割部分：针对指横纹感兴趣区域(ROI)难以准确定位的问题,在明确提出指横纹ROI定义的前提下,提出利用小波多分辨率分析进行指横纹ROI自动检测定位的新算法。该方法利用纹理相似性原理,在高频子图采用基于特征向量和区域生长法产生指横纹的候选子区域集合,然后利用低频子图Radon投影得到的指横纹的区域特征对候选子区域进行验证,最后结合直线拟合手指轮廓得到指横纹在原图的有效位置,最终实现指横纹ROI的精准定位。同时还利用一种改进的Gabor滤波器提取指横纹,该方法在指横纹特征方向求取阶段,以手指轴线方向作为Gabor滤波器方向；在分割阶段,以固定频率对指横纹特征进行Gabor滤波。实验证明该方法更为符合指横纹的纹理特征分割的要求。
第四,在特征提取和匹配部分：提出一种基于修正不变矩和小波多分辨率分析的指横纹特征提取和识别算法。该方法在一次识别阶段,为克服比例因子和旋转角度对矩的影响,提出最小矩形的求矩区域修正方法并对由Radon投影得到的不变矩特征矩阵进行初等行变换确定旋转角度,从而建立候选图像集合。在二次识别阶段,利用小波多分辨率分析对候选图像集合的确定角度Radon投影进行分析,再由得到的加权不变矩特征向量进行最高相似度匹配。实验表明,该方法较传统方法有更好的识别率。同时,为进一步提高识别率,结合模糊理论,提出一种指横纹和掌纹加权信息融合的匹配方法。该方法在掌纹特征提取阶段采用了一种自适应方法定位掌纹ROI,并提出一种二阶段小波多分辨率分析方法分割掌纹,最后与指横纹进行加权融合。该方法利用掌纹和指横纹同属手部纹线,可以通过一次采集得到信息和同时识别处理的特性,提高系统的处理效率、识别率和稳定性。
最后,对指横纹识别系统软件设计的框架、算法参数进行描述并给出单步运行结果,同时对得到的数据进行分析。利用这款指横纹识别系统对两套不同精度的小规模数据库样本进行对象识别的结果,证明该系统能达到识别快速、稳定、准确识别的要求。
The arrival of the information age is improving the status of information security. Being an important method of authentication, biometric identification technology always is the research mainstream. As using the human inherent physiological and behavioral characteristics, biometric identification technology shows great advantage than any other technologies. Traditionally, biometric identification technology has some shortcomings, consider of this situation, this paper brings forward knuckleprint as a new biometric identification technology to overcome the shortcoming and exploit another biometric identification method.
First, this paper analyzes the characteristics of knuckleprint, principles identification system, functional requirement and capability guide line and design scheme. Identification of key technologies knuckleprint also carried out a detailed summary.
Second, in order to solve the problem of accurate location of finger, this paper utilizes a smallest eigenvalue analysis method to locate the finger fiducial point. Based on uncontinuity of curve, this method uses single pixel and a curve which contain the single pixel as analysis object, and then utilizes macroscopical collinearity to judge the target curve is the region of finger fiducial point or not. Owing to this, the smallest eigenvalue of covariance matrix could get from these target pixels. Finally, through the critical point analyzing, the finger fiducial point is located exactly. Emulation experiment shows that this algorithm overcomes disadvantages in traditional method such as time-consuming, complicated and inaccurate. At the same time, a finger segmentation technology based on straight-line fitting is adopted in this paper. This technology utilizes the linetype characteristic of finger contour to realize finger segmentation in circle.
Third, in order to solve the problem of accurate location of region of interest of knuckleprint, by giving the definition of ROI of knuckleprint, this paper brings forward a new automatic detection and location algorithm based on wavelet multi-resolution to locate the ROI of knuckleprint. Based on texture similar theory, this algorithm uses feature vector and regional growth to produce candidate sub-region set in high frequency sub-images, and then utilize Radon projection in low frequency sub-images to verify the candidate sub-region set. Finally, by adopting straight-line fitting technique, the location of ROI of knuckleprint in original image is accurately located. At the same time, a new segmentation method of knuckleprint based on modified Gabor filter is utilized in this paper. This method uses finger axis direction and certain frequencies as Gabor filter's parameters. Emulation experiment shows that this algorithm is more suitable for knuckleprint.
Fourth, a new matching algorithm of knuckleprint based on moment invariants and wavelet multi-resolution analysis is presented in this paper. In the first recognition stage, in order to overcome the defects caused by scale factor and rotation angle to get the set of candidate images, the algorithm not only proposes an idea of minimal rectangle to modify the calculation region of moment invariants but also adopts the elementary row transformation to transform the character matrix of radon projection. In the second recognition stage, the wavelet multi-resolution analysis is adopted to analyze the certain angle projection of candidate images set. Finally, the weighted character of moment invariants is calculated to get the similarity. Emulation experiment shows that this algorithm is more efficient than traditional algorithm. At the same time, a new fusion technology of weighted information from knuckleprint and palmprint is proposed in this paper. First of all, this algorithm proposes a self-adaptive method to locate ROI of palmprint and complete segmentation by double stages wavelet analysis, and then, fuzzy theory is adopted to finish fusion matching.
Finally, this paper describes software design in frame, algorithm parameters and processing results. Two sample databases are utilized to verify the robust of this system, and the results show that this system satisfies requirements of biometric identification system.
本文首先探讨了指横纹的特点,分析了指横纹识别系统的原理、功能需求、性能指标,给出指横纹识别系统的完整设计方案,并对指横纹识别系统及关键技术进行详细的归纳总结。
其次,在手指分割部分：针对手指基准点难于定位的问题,结合最小特征根分析的方法利用曲线的非连续性特征,首先选定手掌轮廓线上的一个像素点和包含此点的一段连续轮廓线作为分析对象,再采用了一个宏观的共线性判定指标判断此段轮廓是不是构成了手指基准点所在的曲线,接着通过这些像素点坐标统计构成的协方差矩阵的最小特征根的值来定位手指基准点区域,最后采用拐点分析法矫正奇异区域得到手指基准点。解决了传统方法依靠求取曲率和低通滤波得到近似手指基准点而存在的耗时大、复杂性高和不精确的问题。同时利用最小二乘法直线拟合技术,拟合四指外轮廓的方法分割单指,该方法利用人体四指外轮廓类似直线的特性,很好的区分各个手指,有效实现单指循环分割识别。
第三,在指横纹分割部分：针对指横纹感兴趣区域(ROI)难以准确定位的问题,在明确提出指横纹ROI定义的前提下,提出利用小波多分辨率分析进行指横纹ROI自动检测定位的新算法。该方法利用纹理相似性原理,在高频子图采用基于特征向量和区域生长法产生指横纹的候选子区域集合,然后利用低频子图Radon投影得到的指横纹的区域特征对候选子区域进行验证,最后结合直线拟合手指轮廓得到指横纹在原图的有效位置,最终实现指横纹ROI的精准定位。同时还利用一种改进的Gabor滤波器提取指横纹,该方法在指横纹特征方向求取阶段,以手指轴线方向作为Gabor滤波器方向；在分割阶段,以固定频率对指横纹特征进行Gabor滤波。实验证明该方法更为符合指横纹的纹理特征分割的要求。
第四,在特征提取和匹配部分：提出一种基于修正不变矩和小波多分辨率分析的指横纹特征提取和识别算法。该方法在一次识别阶段,为克服比例因子和旋转角度对矩的影响,提出最小矩形的求矩区域修正方法并对由Radon投影得到的不变矩特征矩阵进行初等行变换确定旋转角度,从而建立候选图像集合。在二次识别阶段,利用小波多分辨率分析对候选图像集合的确定角度Radon投影进行分析,再由得到的加权不变矩特征向量进行最高相似度匹配。实验表明,该方法较传统方法有更好的识别率。同时,为进一步提高识别率,结合模糊理论,提出一种指横纹和掌纹加权信息融合的匹配方法。该方法在掌纹特征提取阶段采用了一种自适应方法定位掌纹ROI,并提出一种二阶段小波多分辨率分析方法分割掌纹,最后与指横纹进行加权融合。该方法利用掌纹和指横纹同属手部纹线,可以通过一次采集得到信息和同时识别处理的特性,提高系统的处理效率、识别率和稳定性。
最后,对指横纹识别系统软件设计的框架、算法参数进行描述并给出单步运行结果,同时对得到的数据进行分析。利用这款指横纹识别系统对两套不同精度的小规模数据库样本进行对象识别的结果,证明该系统能达到识别快速、稳定、准确识别的要求。
The arrival of the information age is improving the status of information security. Being an important method of authentication, biometric identification technology always is the research mainstream. As using the human inherent physiological and behavioral characteristics, biometric identification technology shows great advantage than any other technologies. Traditionally, biometric identification technology has some shortcomings, consider of this situation, this paper brings forward knuckleprint as a new biometric identification technology to overcome the shortcoming and exploit another biometric identification method.
First, this paper analyzes the characteristics of knuckleprint, principles identification system, functional requirement and capability guide line and design scheme. Identification of key technologies knuckleprint also carried out a detailed summary.
Second, in order to solve the problem of accurate location of finger, this paper utilizes a smallest eigenvalue analysis method to locate the finger fiducial point. Based on uncontinuity of curve, this method uses single pixel and a curve which contain the single pixel as analysis object, and then utilizes macroscopical collinearity to judge the target curve is the region of finger fiducial point or not. Owing to this, the smallest eigenvalue of covariance matrix could get from these target pixels. Finally, through the critical point analyzing, the finger fiducial point is located exactly. Emulation experiment shows that this algorithm overcomes disadvantages in traditional method such as time-consuming, complicated and inaccurate. At the same time, a finger segmentation technology based on straight-line fitting is adopted in this paper. This technology utilizes the linetype characteristic of finger contour to realize finger segmentation in circle.
Third, in order to solve the problem of accurate location of region of interest of knuckleprint, by giving the definition of ROI of knuckleprint, this paper brings forward a new automatic detection and location algorithm based on wavelet multi-resolution to locate the ROI of knuckleprint. Based on texture similar theory, this algorithm uses feature vector and regional growth to produce candidate sub-region set in high frequency sub-images, and then utilize Radon projection in low frequency sub-images to verify the candidate sub-region set. Finally, by adopting straight-line fitting technique, the location of ROI of knuckleprint in original image is accurately located. At the same time, a new segmentation method of knuckleprint based on modified Gabor filter is utilized in this paper. This method uses finger axis direction and certain frequencies as Gabor filter's parameters. Emulation experiment shows that this algorithm is more suitable for knuckleprint.
Fourth, a new matching algorithm of knuckleprint based on moment invariants and wavelet multi-resolution analysis is presented in this paper. In the first recognition stage, in order to overcome the defects caused by scale factor and rotation angle to get the set of candidate images, the algorithm not only proposes an idea of minimal rectangle to modify the calculation region of moment invariants but also adopts the elementary row transformation to transform the character matrix of radon projection. In the second recognition stage, the wavelet multi-resolution analysis is adopted to analyze the certain angle projection of candidate images set. Finally, the weighted character of moment invariants is calculated to get the similarity. Emulation experiment shows that this algorithm is more efficient than traditional algorithm. At the same time, a new fusion technology of weighted information from knuckleprint and palmprint is proposed in this paper. First of all, this algorithm proposes a self-adaptive method to locate ROI of palmprint and complete segmentation by double stages wavelet analysis, and then, fuzzy theory is adopted to finish fusion matching.
Finally, this paper describes software design in frame, algorithm parameters and processing results. Two sample databases are utilized to verify the robust of this system, and the results show that this system satisfies requirements of biometric identification system.
引文
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