指纹识别算法研究及其DSP实现
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摘要
指纹的唯一性和不变性决定了它在身份认证中的重要地位。随着低价位指纹采集仪的出现和高可靠性算法的实现,自动指纹识别技术越来越深入到人们的生活和工作当中。另一方面,由于数字信号处理器(DSP)的迅猛发展,已经可以满足图像处理中的运算量要求。
本文首先讨论了指纹识别的有关概念及常规算法,然后在此基础上提出了一种利用小波变换的指纹识别新算法,该算法利用指纹的子带小波变换系数近似服从广义高斯分布这一特点,从而仅使用两个参数就可代表指纹小波子带的特征,并以此为指纹识别的特征进行匹配。该算法的优点在于:预处理算法简单、处理速度快;算法的数据存储量小(存储6个数,存储量为24字节。近似于现阶段“ID+密码”方式的数据量),适合实际应用。论文进行了大量的MATLAB仿真实验,并与传统算法进行了性能和仿真结果比较。实验表明该算法识别步骤简单、速度快、识别率高。
本文的指纹识别算法研究是以TMS320VC5510DSK为硬件平台进行的,该硬件系统使用FPC1010指纹采集卡实时获取指纹图像,在DSP集成开发环境CCS
(Code Composer Studio)上进行算法的调试。本文介绍了DSP实现的步骤以及实现过程中程序编写所注意的问题、程序的调试方法以及采用的优化策略。
结果表明该算法存储量小、运行速度快、识别率高、可靠性强。为指纹识别算法的发展提供了新的思路。
Fingerprint has important state in identity verification for its exclusivity and invariability. Along with the emergence of the low cost fingerprint sensor and the realization of the highly reliable algorithm, the automated fingerprint identification technique is applied more and more in our life and work. On the other hand, because of the fast development of the digital signal processor (DSP), the DSP already can satisfy the request of abounding computing in image processing.This paper first discusses the relevant concept and the traditional algorithm of fingerprint verification, then put forward a new fingerprint verification algorithm based on wavelet transformation. This algorithm is on the base of the character that the wavelet subband coefficient approximately obedient to the generalized Gaussian density. So two parameters can represent the wavelet subband and can be used to match fingerprints as fingerprint's character. The algorithm's advangtage is simplity of pre-processing algorithm, fast processing speed;small data memory needed by the algorithm (need to save 6 data, 24 byte. Be close to the capacity of 'ID+password'), which suit real application. The paper carries on a great deal of simulation in MATLAB, and compares the performance of this algorithm with the traditional algorithm. The result of simulation show that this algorithm has simple steps、 fast speed and high identification rate.Fingerprint verification algorithm in this paper is on the hardware platform of TMS320VC5510 DSK and FPC1010 fingerprint sensor (for capturing fingerprint), and is debugged in DSP Code Composer Studio (CCS). This paper introduce the steps of the realization on DSP and in the process of realization, the problems considered in programming、 method to debug and optimization technique.The result show that the algorithm needs small memory capacity, and has fast speed、 high identification rate and credibility. It provides a new way to develop fingerprint verification technology.
本文首先讨论了指纹识别的有关概念及常规算法,然后在此基础上提出了一种利用小波变换的指纹识别新算法,该算法利用指纹的子带小波变换系数近似服从广义高斯分布这一特点,从而仅使用两个参数就可代表指纹小波子带的特征,并以此为指纹识别的特征进行匹配。该算法的优点在于:预处理算法简单、处理速度快;算法的数据存储量小(存储6个数,存储量为24字节。近似于现阶段“ID+密码”方式的数据量),适合实际应用。论文进行了大量的MATLAB仿真实验,并与传统算法进行了性能和仿真结果比较。实验表明该算法识别步骤简单、速度快、识别率高。
本文的指纹识别算法研究是以TMS320VC5510DSK为硬件平台进行的,该硬件系统使用FPC1010指纹采集卡实时获取指纹图像,在DSP集成开发环境CCS
(Code Composer Studio)上进行算法的调试。本文介绍了DSP实现的步骤以及实现过程中程序编写所注意的问题、程序的调试方法以及采用的优化策略。
结果表明该算法存储量小、运行速度快、识别率高、可靠性强。为指纹识别算法的发展提供了新的思路。
Fingerprint has important state in identity verification for its exclusivity and invariability. Along with the emergence of the low cost fingerprint sensor and the realization of the highly reliable algorithm, the automated fingerprint identification technique is applied more and more in our life and work. On the other hand, because of the fast development of the digital signal processor (DSP), the DSP already can satisfy the request of abounding computing in image processing.This paper first discusses the relevant concept and the traditional algorithm of fingerprint verification, then put forward a new fingerprint verification algorithm based on wavelet transformation. This algorithm is on the base of the character that the wavelet subband coefficient approximately obedient to the generalized Gaussian density. So two parameters can represent the wavelet subband and can be used to match fingerprints as fingerprint's character. The algorithm's advangtage is simplity of pre-processing algorithm, fast processing speed;small data memory needed by the algorithm (need to save 6 data, 24 byte. Be close to the capacity of 'ID+password'), which suit real application. The paper carries on a great deal of simulation in MATLAB, and compares the performance of this algorithm with the traditional algorithm. The result of simulation show that this algorithm has simple steps、 fast speed and high identification rate.Fingerprint verification algorithm in this paper is on the hardware platform of TMS320VC5510 DSK and FPC1010 fingerprint sensor (for capturing fingerprint), and is debugged in DSP Code Composer Studio (CCS). This paper introduce the steps of the realization on DSP and in the process of realization, the problems considered in programming、 method to debug and optimization technique.The result show that the algorithm needs small memory capacity, and has fast speed、 high identification rate and credibility. It provides a new way to develop fingerprint verification technology.
引文
[1] Huang K, Aviyente S, 《Fingerprint verification based on wavelet subbands》, Biometric technology for human identification, 2004
[2] 汪祖媛,庄镇泉等,《基于形状的小波变换系数广义高斯分布图像检索算法》,电子学报,2003
[3] Qinzhi Zhang, Hong Yan, Fingerprint classification based On extraction and analysis of singularities and pseudo ridge, Pattern Recognition, 2004
[4] 沈伟,陈霞等,《指纹图像奇异点提取的一种鲁棒方法》,计算机工程,2003
[5] 王卫平,吴小强等,《指纹识别中的自动分类研究》,数据采集与处理,2001
[6] Minh N. Do, Martin Vetterli, 《Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance》, IEEETransaction On Image Processing, 2002
[7] 程建刚,田捷等,《基于奇异点的指纹分类》,中国科学院自动化所,2000
[8] 刘小华,王燕生, 《指纹识别技术的发展》,光学技术,1998.7
[9] Ulf Grenander, Anuj Srivastava, 《Probability models for clutter in natural images》, IEEE Transaction On Pattern Analysis and Machine Intelligence, 2001. 4
[10] Texas Instruments, TMS320VC5510 DSK Technical Reference, 2002
[11] Texas Instruments, TMS320C55x Optimizing C/C++ Compiler User's Guide, 2003. 3
[12] Texas Instruments, Code Composer Studio User's Guide, 2000. 1
[13] Texas Instruments, TMS320VC5510 Fixed-Point Digital Signal Processor, 2000. 6
[14] Texas Instruments, TMS320C55x DSP Library Programmer's Reference, 2004. 10
[15] Texas Instruments, TMS320C55x Assembly Language Tools User's Guide, 2003. 3
[16] Texas Instruments, TMS320C55x DSP Peripherals Overview Reference Guide, 2004. 1
[17] Texas Instruments, FPC1010 Finger Print Sensor Daughter Card Technical Reference, 2003
[18] Texas Instruments, FPCore Demo for Texas Instruments TMS320C6713 & TMS320C5510 DSP Starter Kits, 2003. 11
[19] Texas Instruments, FPC1010 Sensor Drivers Quick Start Guide, 2003. 7
[20] 徐飞,施晓红等, 《MATLAB应用图像处理》,西安电子科技大学出版社,2002.5,p207-233
[21] 张勇, 《C/C++语言硬件程序设计——基于TMS320C5000系列DSP》,西安电子科技大学出版社,2003.5,p69-146
[22] 徐士良,《C常用算法程序集》,清华大学出版社,11996.11,p362-363,p285-286
[23] 杨宏林,吴陈,《指纹识别方法的综述》,华东船舶工业学院学报,2003.6
[24] 尹义龙,宁新宝,张晓梅,《自动指纹识别技术的发展与应用》,南京大学学报,2002.1
[25] 吕知辛,黄遵令,《指纹识别方法及其应用》,应用技术,2004
[26] 虞正华,周功禹,余松煜,《CT图像小波变换系数的统计特性》,中国生物医学工程学报,1999.2
[27] 李真芳,苏涛,黄小宇,《DSP程序开发:MATLAB调试及直接目标代码生成》,西安电子科技大学出版社,2003.10
[28] 董长虹,《MATLAB小波分析工具箱原理及应用》,国防工业出版社,2004.1
[29] Haar A, 《Zur Theorie der Orthogonalen Funktionen-Systeme》 Math. Ann, 1910, p331-337
[30] Grosses'A, Morlet J, 《Decomposition of Hardy Functions into Square Wavelets of ConstantShape》, SIAM. J. Math. Anal, 1984(15), pp723-726
[31] Mallat S, 《A theory for Multiresolution Signal Decomposition: The Wavelet Representation》, IEEE Transactions on Patern Analysis and Machine Intelligence, 1989, 11(7), pp674-693
[32] Mallat S, 《Multifrequency Channel Decompositions of Images and Wavelet Models》, IEEE Transactions on Acoustics. Speech and Signal Processing, 1989, 37(12), pp2091-2110
[33] 张雄伟,陈亮,徐光辉, 《DSP集成开发与应用实例》,电子工业出版社, 2002.6,p67-83
[34] 汪春梅,孙洪波,任治刚,《TMS320C5000 DSP系统设计与开发实例》,电了工业出版社,2004.7
[2] 汪祖媛,庄镇泉等,《基于形状的小波变换系数广义高斯分布图像检索算法》,电子学报,2003
[3] Qinzhi Zhang, Hong Yan, Fingerprint classification based On extraction and analysis of singularities and pseudo ridge, Pattern Recognition, 2004
[4] 沈伟,陈霞等,《指纹图像奇异点提取的一种鲁棒方法》,计算机工程,2003
[5] 王卫平,吴小强等,《指纹识别中的自动分类研究》,数据采集与处理,2001
[6] Minh N. Do, Martin Vetterli, 《Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance》, IEEETransaction On Image Processing, 2002
[7] 程建刚,田捷等,《基于奇异点的指纹分类》,中国科学院自动化所,2000
[8] 刘小华,王燕生, 《指纹识别技术的发展》,光学技术,1998.7
[9] Ulf Grenander, Anuj Srivastava, 《Probability models for clutter in natural images》, IEEE Transaction On Pattern Analysis and Machine Intelligence, 2001. 4
[10] Texas Instruments, TMS320VC5510 DSK Technical Reference, 2002
[11] Texas Instruments, TMS320C55x Optimizing C/C++ Compiler User's Guide, 2003. 3
[12] Texas Instruments, Code Composer Studio User's Guide, 2000. 1
[13] Texas Instruments, TMS320VC5510 Fixed-Point Digital Signal Processor, 2000. 6
[14] Texas Instruments, TMS320C55x DSP Library Programmer's Reference, 2004. 10
[15] Texas Instruments, TMS320C55x Assembly Language Tools User's Guide, 2003. 3
[16] Texas Instruments, TMS320C55x DSP Peripherals Overview Reference Guide, 2004. 1
[17] Texas Instruments, FPC1010 Finger Print Sensor Daughter Card Technical Reference, 2003
[18] Texas Instruments, FPCore Demo for Texas Instruments TMS320C6713 & TMS320C5510 DSP Starter Kits, 2003. 11
[19] Texas Instruments, FPC1010 Sensor Drivers Quick Start Guide, 2003. 7
[20] 徐飞,施晓红等, 《MATLAB应用图像处理》,西安电子科技大学出版社,2002.5,p207-233
[21] 张勇, 《C/C++语言硬件程序设计——基于TMS320C5000系列DSP》,西安电子科技大学出版社,2003.5,p69-146
[22] 徐士良,《C常用算法程序集》,清华大学出版社,11996.11,p362-363,p285-286
[23] 杨宏林,吴陈,《指纹识别方法的综述》,华东船舶工业学院学报,2003.6
[24] 尹义龙,宁新宝,张晓梅,《自动指纹识别技术的发展与应用》,南京大学学报,2002.1
[25] 吕知辛,黄遵令,《指纹识别方法及其应用》,应用技术,2004
[26] 虞正华,周功禹,余松煜,《CT图像小波变换系数的统计特性》,中国生物医学工程学报,1999.2
[27] 李真芳,苏涛,黄小宇,《DSP程序开发:MATLAB调试及直接目标代码生成》,西安电子科技大学出版社,2003.10
[28] 董长虹,《MATLAB小波分析工具箱原理及应用》,国防工业出版社,2004.1
[29] Haar A, 《Zur Theorie der Orthogonalen Funktionen-Systeme》 Math. Ann, 1910, p331-337
[30] Grosses'A, Morlet J, 《Decomposition of Hardy Functions into Square Wavelets of ConstantShape》, SIAM. J. Math. Anal, 1984(15), pp723-726
[31] Mallat S, 《A theory for Multiresolution Signal Decomposition: The Wavelet Representation》, IEEE Transactions on Patern Analysis and Machine Intelligence, 1989, 11(7), pp674-693
[32] Mallat S, 《Multifrequency Channel Decompositions of Images and Wavelet Models》, IEEE Transactions on Acoustics. Speech and Signal Processing, 1989, 37(12), pp2091-2110
[33] 张雄伟,陈亮,徐光辉, 《DSP集成开发与应用实例》,电子工业出版社, 2002.6,p67-83
[34] 汪春梅,孙洪波,任治刚,《TMS320C5000 DSP系统设计与开发实例》,电了工业出版社,2004.7
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