小型出入口监控系统中身份验证设计
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摘要
信息技术的飞速发展对身份验证的可信度和便捷度提出了更新的要求,一些传统的身份认证方法已经不能满足这些新的需求,用生物特征来进行身份认证已成为研究热点。
人脸识别是模式识别技术在图像领域中的具体运用,其应用前景非常广阔,作为一种典型的生物特征识别,人脸识别以其自然性、可接受性、采集方便等优点受到了人们的青睐,可以应用到身份证件的鉴别、自动门禁控制系统、刑侦破案、视频监控、家庭安全等领域。
本文首先介绍了现有的人脸识别算法,详细介绍了经典的基于图像向量的PCA人脸识别算法,并针对经典PCA算法在识别当中存在的不足,提出了一种可以提高识别率的PCA算法,即在做算法的识别前加入图像预处理的阶段,这样就能够有效的提高识别率了。然后,在二维主成分分析(2DPCA)的人脸识别算法的基础上提出一种改进的2DPCA的算法(M-2DPCA)。一般情况下,M-2DPCA和2DPCA的识别率接近,高于加入了预处理的PCA算法的识别率。但是M-2DPCA和2DPCA方法在重构时有更高的空间复杂度和时间复杂度,因此重构时要占用更多的内存,当实际应用中系统不能提供足够大内存的情况下,我们就优先选择加入图像预处理的PCA算法。
综上所述,本文实现了基于改进的PCA的人脸识别系统,在系统实现的过程中采用了加入图像预处理的PCA方法,使系统的识别率有所提高。
本文所设计的身份验证系统,除了对算法的优化外,更加重视系统设计,可更好的发挥算法的效能。
Today in the rapid development of information technology, we are made the updated requirements in the authenticated degree of credibility and convenience. Some of the traditional authentication methods clearly can not meet these new demands, and it has become a research hotspot by using biometrics for identity authentication.
Face recognition is a pattern recognition technology in the concrete application of the image in the field, and it is very popular in its application prospect . It has been collected to facilitate the people's favoras as a typical biometric identification for its naturalness, acceptability, convenience. Identity documents can be applied to the identification, automatic access control systems, forensic detection, video surveillance, the security of home and other fields.
This article describes the existing face recognition algorithm, with describing the classic image-based vector PCA face recognition algorithm in detail. Because the classical PCA algorithm identifies those shortcomings,we are presenting an improved method of M-PCA. Before the image pre-processing stage ,the recognition algorithm is done,so that we can effectively improve the recognition rate. Then, an improved 2DPCA algorithm (M-2DPCA), M-2DPCA and 2DPCA recognition rate is close to but higher than the M-PCA recognition rate. Under normal circumstances , M-2DPCA and 2DPCA recognition rate are close to each other.However, M-2DPCA and 2DPCA methods remodeling have a higher degree of spatial complexity and time complexity, so we have to spend more time in the reconstruction of memory. When the practical application of the system can not provide enough memory cases, the superiority of PCA in performance is manifested.
In summary,this paper presents an improved PCA which is based in face recognition system.It uses PCA method in the process of system implementation, mainly because it can effectively improve the system recognition rate.
This article is designed authentication system. In addition to the optimization algorithm, it is greater emphasis on system design, in order to play its algorithm performance.
人脸识别是模式识别技术在图像领域中的具体运用,其应用前景非常广阔,作为一种典型的生物特征识别,人脸识别以其自然性、可接受性、采集方便等优点受到了人们的青睐,可以应用到身份证件的鉴别、自动门禁控制系统、刑侦破案、视频监控、家庭安全等领域。
本文首先介绍了现有的人脸识别算法,详细介绍了经典的基于图像向量的PCA人脸识别算法,并针对经典PCA算法在识别当中存在的不足,提出了一种可以提高识别率的PCA算法,即在做算法的识别前加入图像预处理的阶段,这样就能够有效的提高识别率了。然后,在二维主成分分析(2DPCA)的人脸识别算法的基础上提出一种改进的2DPCA的算法(M-2DPCA)。一般情况下,M-2DPCA和2DPCA的识别率接近,高于加入了预处理的PCA算法的识别率。但是M-2DPCA和2DPCA方法在重构时有更高的空间复杂度和时间复杂度,因此重构时要占用更多的内存,当实际应用中系统不能提供足够大内存的情况下,我们就优先选择加入图像预处理的PCA算法。
综上所述,本文实现了基于改进的PCA的人脸识别系统,在系统实现的过程中采用了加入图像预处理的PCA方法,使系统的识别率有所提高。
本文所设计的身份验证系统,除了对算法的优化外,更加重视系统设计,可更好的发挥算法的效能。
Today in the rapid development of information technology, we are made the updated requirements in the authenticated degree of credibility and convenience. Some of the traditional authentication methods clearly can not meet these new demands, and it has become a research hotspot by using biometrics for identity authentication.
Face recognition is a pattern recognition technology in the concrete application of the image in the field, and it is very popular in its application prospect . It has been collected to facilitate the people's favoras as a typical biometric identification for its naturalness, acceptability, convenience. Identity documents can be applied to the identification, automatic access control systems, forensic detection, video surveillance, the security of home and other fields.
This article describes the existing face recognition algorithm, with describing the classic image-based vector PCA face recognition algorithm in detail. Because the classical PCA algorithm identifies those shortcomings,we are presenting an improved method of M-PCA. Before the image pre-processing stage ,the recognition algorithm is done,so that we can effectively improve the recognition rate. Then, an improved 2DPCA algorithm (M-2DPCA), M-2DPCA and 2DPCA recognition rate is close to but higher than the M-PCA recognition rate. Under normal circumstances , M-2DPCA and 2DPCA recognition rate are close to each other.However, M-2DPCA and 2DPCA methods remodeling have a higher degree of spatial complexity and time complexity, so we have to spend more time in the reconstruction of memory. When the practical application of the system can not provide enough memory cases, the superiority of PCA in performance is manifested.
In summary,this paper presents an improved PCA which is based in face recognition system.It uses PCA method in the process of system implementation, mainly because it can effectively improve the system recognition rate.
This article is designed authentication system. In addition to the optimization algorithm, it is greater emphasis on system design, in order to play its algorithm performance.
引文
[1] A.K.Jan.Biometrics:Promising Frontier for Emerging Identification Market.Communication ACM.2000:91~98
[2]李健,廖秋菊.人脸识别方法的研究.人工智能,2006,22:2-4
[3]罗昊.人脸识别技术研究.[贵州工业大学硕士学位论文],2004
[4]闫娟,程武山,孙鑫.人脸识别的技术研究与发展概况.电视技术,2006,(12):81-84
[5]刘青山,卢汉清,马颂德.综述人脸识别的子空间方法.自动化学, 2003, 29(6): 900-911
[6]王蕴红,谭铁牛.现代身份鉴别新技术—生物特征识别技术.中国基础科学-科学前沿,2000, 9:4-10
[7]包桂秋,林喜荣,苏晓生,等.基于人体生物特征的身份鉴别技术发展概况.清华大学学报(自然科学版),2001,41(4):72-76
[8] X.Q.DING,C.H.FANG.Discussions on Some Problems in Face Recognition. Sino biometrics 2004.Berlin:Springer-Verlag,2004,3338:47-56
[9]山世光.人脸识别中若干关键问题的研究:[中国科学院博士学位论文]:2004
[10] A.S.TOLBA,A.H.EL-BAZ,A.A.EL-HARBY.Face Recognition:A Literature Review.International Journal of Signal Processing.2005,2(2):88-103
[11]肖冰,王映辉.人脸识别研究综述.计算机应用研究.2005,8:1~5
[12] W.ZHAO,R.P.CHELLAPA,P.J.ROSENFELD.Face Recognition: A Literature Survey.ACM,Computing Surveys(CSUR).2003,35(4):399-458
[13] I.COX, Ghosn,J.YIANILOS.Feature-based Face Recognition Using Mixture Distance.Processings Intelligence Conference Computing Vision. Pattern Recognition.1996:209-216
[14] R.P.BRUNELLI,T.FACE.recognition:features versus templates.IEEE Trans.PAMI, 1993,15(10):1042-1052.
[15]何东风,凌捷.人脸识别技术综述.微机发展.2003,12:75-78.
[16] L.HONG , J.ANIL.Integrating Faeesand Fingerprints for Personal Identifiation.IEEE Transactionson Pattern Analysi SandMaehine Intelligenee,1998,20(12)
[17]肖冰,王映辉.人脸识别研究综述.计算机应用研究.2005,8:1~5
[18]刘艳丽,赵跃龙.人脸识别技术研究进展.计算机工程.2005,31(3):10~12
[19] C.CHEN,W.HUANG,L.H.HUMAN.Face Recognition from a Single Front View. Int. Journal of Pattern Recognition ,1992,6(40):571-593
[20] Y.JIAN,Z.DAVID,F.ALEJANDRO,Y.JING-YU.Two-Dimensional PCA:A New Approach toAppearance-Based Face Representation and Recognition.IEEE Trans.on Pattern Analysis and Machine Intelligence.2004,26(1):117-129
[21] M.S.BARTLETT,J.R.MOVELLAN,T.J.SEJNOW.Face Recognition by Independent Component Analysis.IEEE Trans.on Neural Networks.2002,13(6):1450-1464
[22]刘丽华.自动人脸识别方法研究与展望.内江科技.2005,5:57-58
[23]苏宏涛.基于统计特征的人脸识别技术研究.[西北工业大学博士学位论文].2005:8-13
[24] Z.DE-LONG. A Study of Human Face Recogniton Technology. Xi’an: Northwestern Polytechnical University, 2001
[25]李刚,高政.人脸自动识别方法综述.计算机应用研究, 2003, 8: 4-10.
[26] A.TURK,P. PENTLAND. Face Recognition Using Eigenfaces. IEEE Vision and Modeling Group, 1991,5(91):586-591
[27] T.KANADE. Computer recognition of human faces.Basel& Stuttgart:BirkhauserVerlag, 1977.
[28] P.C.YUEN,J.H. Face Representation Using Independent Component Analysis. Pattern Recognition, 2002, 35:1247-1257
[29]王华,李介谷.人脸斜视图象的特征抽取与恢复.上海交通大学学报, 1997, 31(1): 101-104.
[30]郭志波.人脸快速检测和特征抽取方法的研究.南京理工大学, 2007.
[31] T.ALEXANDER, L.YUE,Z.LIJUAN. Exact k-NN queries on clustered SVD datasets. Information Processing Letters, Volume 94, Issue 6,30 June 2005, Pages 247-252
[32] P.JONATHON,H.MOON,S.A.RIZBI,P.J.RAUSS.The Feret Evaluation Methodology for Face Recognition Algorithms.IEEE Trans.Pattern Analysis and Machine Intelligence.2003,22(10):1090-1104
[33] F.SAMARIA,A.HARTER.Parameterization of a Stochastic Model for Human Face Identification.Proceedings of 2nd IEEE Workshop on Applications of Computer Vision.1994:127~131
[34]贾云德.机器视觉.科学出版社,2004:75-80
[35] VYTAUTAS,PERLIBAKAS.Measures for PCA-based Face Recognition.Pattern Recognition Letters.2004,25(6):711-724
[36]章毓晋.图像工程(下)—图像理解与计算机视.北京:清华大学出版社, 2000: 4-10.
[37] CASTLEMAN K.R.数字图像处理.北京:电子工业出版社, 1998: 40-49.
[38]阮秋琦.数字图像处理学.北京:电子工业出版社, 2001.
[39]李世进,郭跃飞,杨静宇.基于多分类组合的人脸识别.数据采集与处理.2000,15(3):293~296
[40] G.STANG.Wavelet transforms vs.Fourier Transforms[J].Bull.Amer.Math.Soe.1993,vol.28
[41] Y.JIAN-GUO.Wavelet Analysis and Its Engineering Applieations,China Machine Press,Beijing,China,2005.
[42] M.C.Lee,C.M.Pun.Texture classification using dominant wavelet packet energy features.Proc.of the 4th IEEE Southwest Symposium on Image Analysis and Interpretation,2000:301-304.
[43] A.NIKOLAIDIS,S.Pita.FaciaifeatureextnlctionandPosedetennination.Pattem Recognition,2000,33:1783-1791.
[44] P.C.YUEN,D.Q.Dai,G.C.Feng.wavelet-based PCA for human face recognition.In: Proeeeding of the IEEE Southwest Symposiuam on Image Analysis and Interpretation,1998,223-228.
[45]闫荣华.基于统计的人脸识别技术研究.西安:西北大学, 2006
[46]赵静,夏良正,舒志强,等.不同光照条件下特征脸方法的改进研究.计算机应用研究, 2005, 22(6): 240-242.
[47] Y.JIAN,Z.David,F.ALEJANDRO,etal.Two-dimensional PCA : A new approach to appearance-based face representation and recognition.IEEE Transaction on Patten Analysis and Machine Intelligence,2004,26(1):131-137.
[48]边肇棋,张学工.模式识别(第二版).清华大学出版社,2000:223~226
[49]阮秋琦.数字图像处理学.北京:电子工业出版社, 2001.1
[50]杨淑莹. VC++图像处理程序设计.北京:清华大学出版社;北京交通大学出版社,2005.1
[51]周长发.精通Visual C++图像处理编程.北京:电子工业出版社,2000
[52]郎锐.数字图像处理学VC++实现.北京:北京希望电子出版社, 2002
[53]席庆,张春林. Visual C++ 6.0实用编程技术.北京:中国水利水电出版社, 1999
[54]张志军,孙志辉.基于VC平台的彩色图像的灰度化技术.自动化技术与应用.2005年第24卷第1期, 61-63
[55]李文革.基于主成分分析的人脸识别. [山东大学硕士学位论文]2008: 23-25
[2]李健,廖秋菊.人脸识别方法的研究.人工智能,2006,22:2-4
[3]罗昊.人脸识别技术研究.[贵州工业大学硕士学位论文],2004
[4]闫娟,程武山,孙鑫.人脸识别的技术研究与发展概况.电视技术,2006,(12):81-84
[5]刘青山,卢汉清,马颂德.综述人脸识别的子空间方法.自动化学, 2003, 29(6): 900-911
[6]王蕴红,谭铁牛.现代身份鉴别新技术—生物特征识别技术.中国基础科学-科学前沿,2000, 9:4-10
[7]包桂秋,林喜荣,苏晓生,等.基于人体生物特征的身份鉴别技术发展概况.清华大学学报(自然科学版),2001,41(4):72-76
[8] X.Q.DING,C.H.FANG.Discussions on Some Problems in Face Recognition. Sino biometrics 2004.Berlin:Springer-Verlag,2004,3338:47-56
[9]山世光.人脸识别中若干关键问题的研究:[中国科学院博士学位论文]:2004
[10] A.S.TOLBA,A.H.EL-BAZ,A.A.EL-HARBY.Face Recognition:A Literature Review.International Journal of Signal Processing.2005,2(2):88-103
[11]肖冰,王映辉.人脸识别研究综述.计算机应用研究.2005,8:1~5
[12] W.ZHAO,R.P.CHELLAPA,P.J.ROSENFELD.Face Recognition: A Literature Survey.ACM,Computing Surveys(CSUR).2003,35(4):399-458
[13] I.COX, Ghosn,J.YIANILOS.Feature-based Face Recognition Using Mixture Distance.Processings Intelligence Conference Computing Vision. Pattern Recognition.1996:209-216
[14] R.P.BRUNELLI,T.FACE.recognition:features versus templates.IEEE Trans.PAMI, 1993,15(10):1042-1052.
[15]何东风,凌捷.人脸识别技术综述.微机发展.2003,12:75-78.
[16] L.HONG , J.ANIL.Integrating Faeesand Fingerprints for Personal Identifiation.IEEE Transactionson Pattern Analysi SandMaehine Intelligenee,1998,20(12)
[17]肖冰,王映辉.人脸识别研究综述.计算机应用研究.2005,8:1~5
[18]刘艳丽,赵跃龙.人脸识别技术研究进展.计算机工程.2005,31(3):10~12
[19] C.CHEN,W.HUANG,L.H.HUMAN.Face Recognition from a Single Front View. Int. Journal of Pattern Recognition ,1992,6(40):571-593
[20] Y.JIAN,Z.DAVID,F.ALEJANDRO,Y.JING-YU.Two-Dimensional PCA:A New Approach toAppearance-Based Face Representation and Recognition.IEEE Trans.on Pattern Analysis and Machine Intelligence.2004,26(1):117-129
[21] M.S.BARTLETT,J.R.MOVELLAN,T.J.SEJNOW.Face Recognition by Independent Component Analysis.IEEE Trans.on Neural Networks.2002,13(6):1450-1464
[22]刘丽华.自动人脸识别方法研究与展望.内江科技.2005,5:57-58
[23]苏宏涛.基于统计特征的人脸识别技术研究.[西北工业大学博士学位论文].2005:8-13
[24] Z.DE-LONG. A Study of Human Face Recogniton Technology. Xi’an: Northwestern Polytechnical University, 2001
[25]李刚,高政.人脸自动识别方法综述.计算机应用研究, 2003, 8: 4-10.
[26] A.TURK,P. PENTLAND. Face Recognition Using Eigenfaces. IEEE Vision and Modeling Group, 1991,5(91):586-591
[27] T.KANADE. Computer recognition of human faces.Basel& Stuttgart:BirkhauserVerlag, 1977.
[28] P.C.YUEN,J.H. Face Representation Using Independent Component Analysis. Pattern Recognition, 2002, 35:1247-1257
[29]王华,李介谷.人脸斜视图象的特征抽取与恢复.上海交通大学学报, 1997, 31(1): 101-104.
[30]郭志波.人脸快速检测和特征抽取方法的研究.南京理工大学, 2007.
[31] T.ALEXANDER, L.YUE,Z.LIJUAN. Exact k-NN queries on clustered SVD datasets. Information Processing Letters, Volume 94, Issue 6,30 June 2005, Pages 247-252
[32] P.JONATHON,H.MOON,S.A.RIZBI,P.J.RAUSS.The Feret Evaluation Methodology for Face Recognition Algorithms.IEEE Trans.Pattern Analysis and Machine Intelligence.2003,22(10):1090-1104
[33] F.SAMARIA,A.HARTER.Parameterization of a Stochastic Model for Human Face Identification.Proceedings of 2nd IEEE Workshop on Applications of Computer Vision.1994:127~131
[34]贾云德.机器视觉.科学出版社,2004:75-80
[35] VYTAUTAS,PERLIBAKAS.Measures for PCA-based Face Recognition.Pattern Recognition Letters.2004,25(6):711-724
[36]章毓晋.图像工程(下)—图像理解与计算机视.北京:清华大学出版社, 2000: 4-10.
[37] CASTLEMAN K.R.数字图像处理.北京:电子工业出版社, 1998: 40-49.
[38]阮秋琦.数字图像处理学.北京:电子工业出版社, 2001.
[39]李世进,郭跃飞,杨静宇.基于多分类组合的人脸识别.数据采集与处理.2000,15(3):293~296
[40] G.STANG.Wavelet transforms vs.Fourier Transforms[J].Bull.Amer.Math.Soe.1993,vol.28
[41] Y.JIAN-GUO.Wavelet Analysis and Its Engineering Applieations,China Machine Press,Beijing,China,2005.
[42] M.C.Lee,C.M.Pun.Texture classification using dominant wavelet packet energy features.Proc.of the 4th IEEE Southwest Symposium on Image Analysis and Interpretation,2000:301-304.
[43] A.NIKOLAIDIS,S.Pita.FaciaifeatureextnlctionandPosedetennination.Pattem Recognition,2000,33:1783-1791.
[44] P.C.YUEN,D.Q.Dai,G.C.Feng.wavelet-based PCA for human face recognition.In: Proeeeding of the IEEE Southwest Symposiuam on Image Analysis and Interpretation,1998,223-228.
[45]闫荣华.基于统计的人脸识别技术研究.西安:西北大学, 2006
[46]赵静,夏良正,舒志强,等.不同光照条件下特征脸方法的改进研究.计算机应用研究, 2005, 22(6): 240-242.
[47] Y.JIAN,Z.David,F.ALEJANDRO,etal.Two-dimensional PCA : A new approach to appearance-based face representation and recognition.IEEE Transaction on Patten Analysis and Machine Intelligence,2004,26(1):131-137.
[48]边肇棋,张学工.模式识别(第二版).清华大学出版社,2000:223~226
[49]阮秋琦.数字图像处理学.北京:电子工业出版社, 2001.1
[50]杨淑莹. VC++图像处理程序设计.北京:清华大学出版社;北京交通大学出版社,2005.1
[51]周长发.精通Visual C++图像处理编程.北京:电子工业出版社,2000
[52]郎锐.数字图像处理学VC++实现.北京:北京希望电子出版社, 2002
[53]席庆,张春林. Visual C++ 6.0实用编程技术.北京:中国水利水电出版社, 1999
[54]张志军,孙志辉.基于VC平台的彩色图像的灰度化技术.自动化技术与应用.2005年第24卷第1期, 61-63
[55]李文革.基于主成分分析的人脸识别. [山东大学硕士学位论文]2008: 23-25