智能楼宇的指纹门禁系统
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摘要
在信息化的今天,人们在享受着信息化带来好处的同时,同时也带来了诸多更多的信息安全方面的隐患。传统的个人身份识别方法越来越受到局限,而指纹识别作为一种安全可靠,可行性高的生物识别技术得到了极大的发展,目前指纹识别技术已经被广泛应用于司法、公安和各种安全防护系统。因此,进行指纹识别技术方面的研究,具有较高的现实意义和理论意义。
文章主要是以指纹识别系统设计为主线而展开的,重点阐述了指纹识别系统的硬件设计和算法设计,对关键算法进行了改进。
首先阐述了生物识别技术的历史和各种生物特征识别技术的特点,着重介绍了指纹识别技术的发展过程,指明了指纹识别技术发展的方向。
然后,介绍了两种指纹特征——细节特征和纹理特征,并比较了基于这两种不同指纹特征的指纹识别方法的优劣,同时给出了评价指纹识别性能的指标和自动指纹识别系统的流程图
其次,在指纹门禁这个应用背景下,提出了一套基于TMS320VC5509A的指纹识别系统的硬件设计方案,并分别对指纹采集模块、指纹处理模块、CPLD接口模块、存储器扩展、电源模块和CAN总线数据通信模块进行了详细讨论。
再次,抓住基于纹理特征的指纹识别方法简单且计算量小的特点,对该类指纹识别算法进行了系统研究和实现:
(1)对于传统的块方向图方向不连续的现象,提出了一套新颖的方向图修正算法,该算法对于噪声相对较小的情况具有良好的效果;对多窗口法求块方向图给出了减少运算量的方法。
(2)在方向滤波器设计方面,从Gabor函数的快速衰减性和方向性得到启发,给出了一个方向滤波器的闭合等式,不仅可以简化方向滤波器设计,而且相对于Gabor滤波而言,可以大大减少运算量,并取得了较好的增强效果。
(3)在中心点求取方面,借鉴现有方法,将中心点的搜索分为两级实现:粗搜索和细搜索。提出了一种新的粗搜索算法,可以减少计算量,加快中心区域搜索速度,最终获得了较为满意的结果。
最后,分析了本文指纹识别算法存在的问题,对课题的进一步研究给出了参考意见。
Today, it has been more convenience than before in informatization world, followed with many information security risks. The traditional identification methods have faced more and more challenges, while the fingerprint identification has gotten a great development as a reliable and highly feasible biometric identification technology. The research of the fingerprint identification technology has great important in practice and theory. Now, it has been applied in many fields such as justice, public security and security systems and so on.
The design of fingerprint identification system has been introduced in this paper, and we emphasized on the hardware and algorithm design and the key algorithm has been improved.
Firstly, different kinds of biometric identification technology have been introduced. And the development of fingerprint identification technology has also been presented in detail. Furthermore, the way of development in fingerprint identification technology has been pointed out
Secondly, after introducing the minutia and texture characters, the advantages and the disadvantages of two fingerprint identification technology, the performance standards and the flow chart of AFIS had been presented in this paper
Thirdly, this paper advanced a hardware design of fingerprint identification system based on TMS320VC5509A. And the system contains some modules such as fingerprint acquisition, fingerprint data process, CPLD as interface, memory extension, power supply and CANBUS.
In additionally, some research had been carried on the fingerprint identification arithmetic based on the texture characters, as following:
1. Because of the discontinuity in the traditional block orientation image, we present a new fingerprint orientation image modification algorithm which has good performance in the low-noise condition. And a method for multi-window algorithm is effective in reducing the time of calculation.
2. Inspired from Gabor function, we give out a closed equation of directional filter which can not only simplify design of the directional filter, but also greatly reduce the amount of computation. The results showed that this method is effective.
3. Referring to existing algorithm, the process of core point's searching is divided into two steps—rough searching and fine searching. A new rough search algorithm can also reduce the amount of computation and accelerate the process of central region searching.
Finally, some feasible suggestion for the shortcomings of fingerprint identification algorithm has been put forward.
文章主要是以指纹识别系统设计为主线而展开的,重点阐述了指纹识别系统的硬件设计和算法设计,对关键算法进行了改进。
首先阐述了生物识别技术的历史和各种生物特征识别技术的特点,着重介绍了指纹识别技术的发展过程,指明了指纹识别技术发展的方向。
然后,介绍了两种指纹特征——细节特征和纹理特征,并比较了基于这两种不同指纹特征的指纹识别方法的优劣,同时给出了评价指纹识别性能的指标和自动指纹识别系统的流程图
其次,在指纹门禁这个应用背景下,提出了一套基于TMS320VC5509A的指纹识别系统的硬件设计方案,并分别对指纹采集模块、指纹处理模块、CPLD接口模块、存储器扩展、电源模块和CAN总线数据通信模块进行了详细讨论。
再次,抓住基于纹理特征的指纹识别方法简单且计算量小的特点,对该类指纹识别算法进行了系统研究和实现:
(1)对于传统的块方向图方向不连续的现象,提出了一套新颖的方向图修正算法,该算法对于噪声相对较小的情况具有良好的效果;对多窗口法求块方向图给出了减少运算量的方法。
(2)在方向滤波器设计方面,从Gabor函数的快速衰减性和方向性得到启发,给出了一个方向滤波器的闭合等式,不仅可以简化方向滤波器设计,而且相对于Gabor滤波而言,可以大大减少运算量,并取得了较好的增强效果。
(3)在中心点求取方面,借鉴现有方法,将中心点的搜索分为两级实现:粗搜索和细搜索。提出了一种新的粗搜索算法,可以减少计算量,加快中心区域搜索速度,最终获得了较为满意的结果。
最后,分析了本文指纹识别算法存在的问题,对课题的进一步研究给出了参考意见。
Today, it has been more convenience than before in informatization world, followed with many information security risks. The traditional identification methods have faced more and more challenges, while the fingerprint identification has gotten a great development as a reliable and highly feasible biometric identification technology. The research of the fingerprint identification technology has great important in practice and theory. Now, it has been applied in many fields such as justice, public security and security systems and so on.
The design of fingerprint identification system has been introduced in this paper, and we emphasized on the hardware and algorithm design and the key algorithm has been improved.
Firstly, different kinds of biometric identification technology have been introduced. And the development of fingerprint identification technology has also been presented in detail. Furthermore, the way of development in fingerprint identification technology has been pointed out
Secondly, after introducing the minutia and texture characters, the advantages and the disadvantages of two fingerprint identification technology, the performance standards and the flow chart of AFIS had been presented in this paper
Thirdly, this paper advanced a hardware design of fingerprint identification system based on TMS320VC5509A. And the system contains some modules such as fingerprint acquisition, fingerprint data process, CPLD as interface, memory extension, power supply and CANBUS.
In additionally, some research had been carried on the fingerprint identification arithmetic based on the texture characters, as following:
1. Because of the discontinuity in the traditional block orientation image, we present a new fingerprint orientation image modification algorithm which has good performance in the low-noise condition. And a method for multi-window algorithm is effective in reducing the time of calculation.
2. Inspired from Gabor function, we give out a closed equation of directional filter which can not only simplify design of the directional filter, but also greatly reduce the amount of computation. The results showed that this method is effective.
3. Referring to existing algorithm, the process of core point's searching is divided into two steps—rough searching and fine searching. A new rough search algorithm can also reduce the amount of computation and accelerate the process of central region searching.
Finally, some feasible suggestion for the shortcomings of fingerprint identification algorithm has been put forward.
引文
[1] Dapeng Zhang, Wei Shu. Two Novel Characteristics in Palmprint Verification: Datum Point Invariance and Line Feature Matching. Pattern Recognition, 1999, 32(4): 691-702
[2] Jun Zhang, Yong Yan, Martin Lades. Face Recognition: Eigenface, Elastic Matching and Neural Nets. Proceeding of the IEEE, 1997, 85(9):1423-1435
[3] Richard P. Wilds. Iris Recognition: An Emerging Biometrics Technology. Proceedings of the IEEE, 1997, 85(9): 1348-1363
[4] Joseph P. Campbell JR. Speaker Recognition: A Tutorial. Proceedings of the IEEE, 1997, 85(9):1437-1462
[5] Vishvjit S. Nalwa. Automatic On-line Signature Verification. Proceeding of the IEEE, 1997, 85(2):215-239
[6] Clarke R. Human. Identification in information systems: Management challenges and pulic policy issues. Information Technology and People, 1994, 7(4):6-37
[7] Davies S G. Touching big brother: How biometric technology will fuse flesh and machine. Information Technology and People, 1994, 7(4): 60-69
[8] Miller B. Vital signs of identity. IEEE Spectrum, 1994, 31(2):2230-2246
[9] Newham E. The biometric report. New York: SJB Services, 1995
[10] Lin Hong. Automatic personal identification using fingerprints. Dissertation for Ph. D. [ D ]. Michigan State University, 1998:45-46.
[11] J.G. Daugman. High Confidence Recognition of Persons by a Test of Statistical Independence. IEEE Trans. Pattern Anal, and Machine Intell., vol.15, No.11, pp.1148-1161,1993
[12] A.K. Jain, S. Prabhakar, L. Hong, and S. Pankanti. FingerCode: A filterbank for fingerprint representation and matching. Proc. IEEE Conf, on Computer Vision and Pattern Recognition, vol.2, pp.187-193,1999
[13] Lawrence O'Gorman and Jeffrey V.nickerson. An approach to fingerprint filter design.Pattern Recognition,1989,vol.22,no.1,pp.29-38
[14]L.Hong,Anil K Jain,S.Pankanti and R.BoLLE.Fingerprint enhancement.Proc.1st IEEE WACV,1996,pp.202-207
[15]Nalini K.Ratha,Shaoyun Chen,and Anil K.Jain.Adaptive flow orientaion-based feature extraction in fingerpint images.Pattern Recongnition,1995,vol.28,no.11,pp.1657-1672
[16]Louis Coetzee and Elizabeth Botha.Fingerprint recognition in quality images.Pattern Recognition,1993,vol.26,no.10,pp.1441-1460
[18]D.C.Douglas Hung.Enhancement and feature purification of fingprint images.Pattern Recogintion,1993,vol.26,no.11,pp.1661-1671
[19]Qinghan xiao and Hazem Raafa.Fingprint image postprocessing:a combined statistical and structural approach.Pattern Recogintion,1991,vol.24,no.10,pp.985-992
[20]Miao D,Maltoni D.Direct Gray-Scale Minutia Detection in Fingerprint.IEEE Trans.PAMI,1997,19(1):27-39
[21]Weicheng Shen,Marc Surette and Raj iv Khanna.Evaluation of Automated Biometrics-Based Identification and Verification Systems.Proceedings of the IEEE,1997,85(9):1464-1480
[22]J.L.Wayman.Fundamentals of Biometric Authentication Technologies.International Journal of Image and Graphics,Vol.1,No.1,pp.93-113,2001
[23]Veridicom,Inc.,FPS200 Solid-State Fingerprint Sensor,02-0097-01 Rev2.1,02/15/02
[24]沙占友,集成化智能传感器原理与应用,电子工业出版社,第11章,P236
[25]TEXAS INSTRUMENTS,TMS320VC5509A Hardware Designers'S Resource Guid,SPRAA30,June 2004
[26]TEXAS INSTRUMENTS,TMS320VC5509A Fixed-Point Digital Signal Processor(Data Manual),SPRS205H,November 2002
[27]TEXAS INSTRUMENTS,TMS320VC5509A Digital Signal Processor Silicon Errata,SPRZ200D,June 2003
[28]冯涛,王程;可编程逻辑器件开发技术-MAX+PLUSⅡ入门与提高;北京:人民邮电出版社,2002
[29]侯伯亨,顾新;VHDL硬件描述语言与数字逻辑电路设计;西安:西安电子科技大学出版社,1998
[30]徐志军,徐光辉;CPLD FPGA的开发与应用;北京:电子工业出版社,2002
[31]Altera Corporation,MAX 3000A Programmable Logic Device Family(Data Sheet),ver.3.5,June 2006
[32]Micron Technology,Inc.,MICRON SEMICONDUCTOR PRODUCTS 64Mb:x4,x8,x16 SDRAM,RevB.p65-Pub.10/00,2000
[33]AMD,Am29LVl60D 16 Megabit(2Mx8-Bit/1Mx 16-Bit)CMOS 3.0 Volt-only Boot Sector Flash Memory(Data Sheet),Publication Number 22358 Revsion B,October 7,2004
[34]TEXAS INSTRUMENTS,TPS6200 HIGH-EFFICIENCY STEP-DOWN LOW POWER DC-DC CONVERTER,SLVS294C,SEPTEMBER 2000
[35]TEXAS INSTRUMENTS,TPS76733QD FAST-TRANSIENT-RESPONSE 1A LOW-DROPOUT VOLTAGE REGULATORS,SLVS208C,MAY1999
[36]TEXAS INSTRUMENTS,TPS60110 REGULATED 5-V 300-mA LOW-NOISE CHARGE PUMP DC/DC CONVERTER,SLVS215A,JUNE 1999
[37]TEXAS INSTRUMENTS,SJA100 Stand-alone CAN controller(DATA SHEET),ICl8,Jan 04 2000
[38]TEXAS INSTRUMENTS,6N137 OPTOCOUPLER/OPTOISOLATOR,SQOS003 D2018,JULY 1986
[39]TEXAS INSTRUMENTS,PCA82C250 CAN controller interface(DATA SHEET),IC18,Jan 13/2000
[40]刘微,独立CAN器件SJA1000的应用,南京工程学院学报,Vol.2,No.1,Mar.2002:32-35
[41]张绍忠,王辉,基于SJA1000的CAN总线智能节点实现,电子技术应用,2006年第8期:22-25
[42]伍伟杰,叶邦彦,基于CAN总线的节水灌溉自控系统设计与研究,2006年 第1期:13-15
[43]饶运涛,邹继军,郑勇芸,现场总线CAN原理与应用技术[M],第1版,北京:北京航空航天大学出版社,2003
[44]Jain A K,Lin Hong,Bolle R.On-line fingerprint verifcation.IEEE Transaction on PAMI,1997,19(4):302-314
[45]王珏,石纯一,机器学习研究[J],广西师范大学学报,自然科学版,2003,21(2):1-15
[46]MEHTRE B M,MURTHY N N,KAPOOR S,et al.Segmentation of fingerprint image using the dire.ctional images[J].Pattern Recognition,1987,20(4):429-435
[47]MEHTRE B M,CHATTERJEE B.Segmentation of fingerprint images-a composite method[J].Pattern Recognition.1995,28:1657-1672
[48]Ratha N K,Karu K,Chen Shaoyun,et al.A real-time matching system for large fingerprint database[J].Pattern Analysis and Machine Intelligence,1996,18(8):799-813
[49]Cho B-H,Kim J S,Bae J H,et al.Fingerprint image classification by core analysisi[J].Proceddings of ICSP,2000(60):1534-1537
[50]Caudela G T,Grother P J.PCASYS-A Pattern-level Classification Automation System for Fingerprints[R].[s.l.]:Technical Report NISTIR 567,1995
[51]O'Gorman L,Nickerson J.An Approach to Fingerprint Filter Design[J].Pattern Recognition,1989,22(1):29-38
[52]Sherlock D B G Monro D M,Millard K.Fingerprint Enhancment by Directional Fourier Filtering[J],Proc.Inst.Elect.Eng.Visual Image Signal Processing,1994,141(2):87-94
[53]Douglas Hung DC.Enhancement feature purification of fingerpritn images[J],Patter Recognition,1993,26(11):1661-1671
[54]Lin Hong,Wan Yifei,Anil J.Fingerprint image enhancement:algorithm and performance evaluation[J].IEEE Transaction on Pattern Analysis and Machine Intelligence,1998,20(8):777-789
[55]S.Greenberg,M.Aladjem,D.Kogan and I.Dimitrov.Fingerprint image enhancement using filtering techniques.Real-time Imaging,2002,8(3):227-236
[56]L.Hong,Y Wan and A.K.Jain.Fingerprint image enhancement:algorithm and performanceevaluation.IEEE Trans.on Pattern Analysis and Machine Intelligence,1998,20(8):777-789
[57]J.Yang,L.Liu,T.Jiang and Y Fan.A modified Gabor filter design method for fingerprint image enhancement.Pattern Recognition Letters,2003,24(12):1805-1817
[58]张建伟,陆亨力,指纹自动识别系统中图像分割方法的研究[J],微型电脑应用,1999,15(12):20-22
[59]罗希平,天捷,自动指纹识别中的图像增强和细节匹配算法[J],软件学报,2002,13(5):946-956
[60]Kawagoe M,Tojo A.Fingerprint pattern classification.Pattern Recognition,1984,17(3):295-303
[61]Jain A K,Hong L,Pankanti S.An identity authentication system using fingerprints.Proc.of the IEEE,1997,85(9):1365-1388
[62]Jain A K,Pankanti S,Hong L,et al.Filterbank-based fingerprint matching.IEEE Transactions on Image Processing,2000,9(5):846-859
[2] Jun Zhang, Yong Yan, Martin Lades. Face Recognition: Eigenface, Elastic Matching and Neural Nets. Proceeding of the IEEE, 1997, 85(9):1423-1435
[3] Richard P. Wilds. Iris Recognition: An Emerging Biometrics Technology. Proceedings of the IEEE, 1997, 85(9): 1348-1363
[4] Joseph P. Campbell JR. Speaker Recognition: A Tutorial. Proceedings of the IEEE, 1997, 85(9):1437-1462
[5] Vishvjit S. Nalwa. Automatic On-line Signature Verification. Proceeding of the IEEE, 1997, 85(2):215-239
[6] Clarke R. Human. Identification in information systems: Management challenges and pulic policy issues. Information Technology and People, 1994, 7(4):6-37
[7] Davies S G. Touching big brother: How biometric technology will fuse flesh and machine. Information Technology and People, 1994, 7(4): 60-69
[8] Miller B. Vital signs of identity. IEEE Spectrum, 1994, 31(2):2230-2246
[9] Newham E. The biometric report. New York: SJB Services, 1995
[10] Lin Hong. Automatic personal identification using fingerprints. Dissertation for Ph. D. [ D ]. Michigan State University, 1998:45-46.
[11] J.G. Daugman. High Confidence Recognition of Persons by a Test of Statistical Independence. IEEE Trans. Pattern Anal, and Machine Intell., vol.15, No.11, pp.1148-1161,1993
[12] A.K. Jain, S. Prabhakar, L. Hong, and S. Pankanti. FingerCode: A filterbank for fingerprint representation and matching. Proc. IEEE Conf, on Computer Vision and Pattern Recognition, vol.2, pp.187-193,1999
[13] Lawrence O'Gorman and Jeffrey V.nickerson. An approach to fingerprint filter design.Pattern Recognition,1989,vol.22,no.1,pp.29-38
[14]L.Hong,Anil K Jain,S.Pankanti and R.BoLLE.Fingerprint enhancement.Proc.1st IEEE WACV,1996,pp.202-207
[15]Nalini K.Ratha,Shaoyun Chen,and Anil K.Jain.Adaptive flow orientaion-based feature extraction in fingerpint images.Pattern Recongnition,1995,vol.28,no.11,pp.1657-1672
[16]Louis Coetzee and Elizabeth Botha.Fingerprint recognition in quality images.Pattern Recognition,1993,vol.26,no.10,pp.1441-1460
[18]D.C.Douglas Hung.Enhancement and feature purification of fingprint images.Pattern Recogintion,1993,vol.26,no.11,pp.1661-1671
[19]Qinghan xiao and Hazem Raafa.Fingprint image postprocessing:a combined statistical and structural approach.Pattern Recogintion,1991,vol.24,no.10,pp.985-992
[20]Miao D,Maltoni D.Direct Gray-Scale Minutia Detection in Fingerprint.IEEE Trans.PAMI,1997,19(1):27-39
[21]Weicheng Shen,Marc Surette and Raj iv Khanna.Evaluation of Automated Biometrics-Based Identification and Verification Systems.Proceedings of the IEEE,1997,85(9):1464-1480
[22]J.L.Wayman.Fundamentals of Biometric Authentication Technologies.International Journal of Image and Graphics,Vol.1,No.1,pp.93-113,2001
[23]Veridicom,Inc.,FPS200 Solid-State Fingerprint Sensor,02-0097-01 Rev2.1,02/15/02
[24]沙占友,集成化智能传感器原理与应用,电子工业出版社,第11章,P236
[25]TEXAS INSTRUMENTS,TMS320VC5509A Hardware Designers'S Resource Guid,SPRAA30,June 2004
[26]TEXAS INSTRUMENTS,TMS320VC5509A Fixed-Point Digital Signal Processor(Data Manual),SPRS205H,November 2002
[27]TEXAS INSTRUMENTS,TMS320VC5509A Digital Signal Processor Silicon Errata,SPRZ200D,June 2003
[28]冯涛,王程;可编程逻辑器件开发技术-MAX+PLUSⅡ入门与提高;北京:人民邮电出版社,2002
[29]侯伯亨,顾新;VHDL硬件描述语言与数字逻辑电路设计;西安:西安电子科技大学出版社,1998
[30]徐志军,徐光辉;CPLD FPGA的开发与应用;北京:电子工业出版社,2002
[31]Altera Corporation,MAX 3000A Programmable Logic Device Family(Data Sheet),ver.3.5,June 2006
[32]Micron Technology,Inc.,MICRON SEMICONDUCTOR PRODUCTS 64Mb:x4,x8,x16 SDRAM,RevB.p65-Pub.10/00,2000
[33]AMD,Am29LVl60D 16 Megabit(2Mx8-Bit/1Mx 16-Bit)CMOS 3.0 Volt-only Boot Sector Flash Memory(Data Sheet),Publication Number 22358 Revsion B,October 7,2004
[34]TEXAS INSTRUMENTS,TPS6200 HIGH-EFFICIENCY STEP-DOWN LOW POWER DC-DC CONVERTER,SLVS294C,SEPTEMBER 2000
[35]TEXAS INSTRUMENTS,TPS76733QD FAST-TRANSIENT-RESPONSE 1A LOW-DROPOUT VOLTAGE REGULATORS,SLVS208C,MAY1999
[36]TEXAS INSTRUMENTS,TPS60110 REGULATED 5-V 300-mA LOW-NOISE CHARGE PUMP DC/DC CONVERTER,SLVS215A,JUNE 1999
[37]TEXAS INSTRUMENTS,SJA100 Stand-alone CAN controller(DATA SHEET),ICl8,Jan 04 2000
[38]TEXAS INSTRUMENTS,6N137 OPTOCOUPLER/OPTOISOLATOR,SQOS003 D2018,JULY 1986
[39]TEXAS INSTRUMENTS,PCA82C250 CAN controller interface(DATA SHEET),IC18,Jan 13/2000
[40]刘微,独立CAN器件SJA1000的应用,南京工程学院学报,Vol.2,No.1,Mar.2002:32-35
[41]张绍忠,王辉,基于SJA1000的CAN总线智能节点实现,电子技术应用,2006年第8期:22-25
[42]伍伟杰,叶邦彦,基于CAN总线的节水灌溉自控系统设计与研究,2006年 第1期:13-15
[43]饶运涛,邹继军,郑勇芸,现场总线CAN原理与应用技术[M],第1版,北京:北京航空航天大学出版社,2003
[44]Jain A K,Lin Hong,Bolle R.On-line fingerprint verifcation.IEEE Transaction on PAMI,1997,19(4):302-314
[45]王珏,石纯一,机器学习研究[J],广西师范大学学报,自然科学版,2003,21(2):1-15
[46]MEHTRE B M,MURTHY N N,KAPOOR S,et al.Segmentation of fingerprint image using the dire.ctional images[J].Pattern Recognition,1987,20(4):429-435
[47]MEHTRE B M,CHATTERJEE B.Segmentation of fingerprint images-a composite method[J].Pattern Recognition.1995,28:1657-1672
[48]Ratha N K,Karu K,Chen Shaoyun,et al.A real-time matching system for large fingerprint database[J].Pattern Analysis and Machine Intelligence,1996,18(8):799-813
[49]Cho B-H,Kim J S,Bae J H,et al.Fingerprint image classification by core analysisi[J].Proceddings of ICSP,2000(60):1534-1537
[50]Caudela G T,Grother P J.PCASYS-A Pattern-level Classification Automation System for Fingerprints[R].[s.l.]:Technical Report NISTIR 567,1995
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