脑电信号在身份识别技术中的应用研究
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摘要
生物特征识别在生活和工作中的应用越来越广,人们对生物特征识别的安全性也有了更高的要求。诸如指纹、人脸等生物特征模态,虽然目前已有很广泛的应用,但是在活体检测、抗伪造、抗胁迫方面存在不足。因此,我们对脑电信号在身份识别技术中的应用展开了研究。脑电信号只能来自活体,具有抗伪造、抗胁迫等优点,是一种很有前景的生物特征模态。
红外视频瞳孔定位系统是计算机视觉方面的应用项目,该项目的目标是准确地定位红外视频中瞳孔的中心位置。红外视频瞳孔定位具有广泛的实际应用,本文的定位结果用于良性阵发性位置性眩晕诊断治疗系统,本系统已经在部分医院的设备中得到了应用。
本文的工作分为两个部分：脑电波身份识别和红外视频瞳孔定位。主要内容包括：
(1)在脑电信号特征提取和分类器方面,对AR系数特征和功率谱密度特征在新的数据集上做特征选择,使用希尔伯特谱作为新的特征；将基于稀疏表示的分类器引入到基于脑电信号的身份识别系统中；
(2)构建了基于SVM的开集身份识别系统,弥补了k-NN分类器不能用于开集的不足。实验结果表明基于SVM的开集身份识别系统具有较低的EER,并且识别准确率也与同样数据下的k-NN分类器的识别准确率相当；
(3)实现了两种基于EEG的多模态抗伪造身份识别系统。融合后的系统使得指纹和脑电信号这两种生物特征模态优势互补,系统既具有指纹识别的高准确率,又具有脑电信号的抗伪造性,大大提高了安全性能；
(4)脑电信号中的肌电信号一般都被认为是噪声,本文创新性地利用肌电信号搭建了隐蔽报警系统,实现了基于时域的隐蔽报警检测算法和基于希尔伯特黄变换的隐蔽报警检测算法,两者均能准确地检测脑电信号中的隐蔽报警信号,验证了基于脑电信号的隐蔽报警系统的可行性；
(5)在红外视频瞳孔定位项目中很好地权衡了定位准确率与算法效率,实现了高准确率的实时瞳孔定位系统。改进了中值滤波算法、Hough变换圆检测算法和Daugman圆检测算子,在保证准确率的前提下,降低了计算复杂度。
As biometrics being used more and more widely in our daily life, the demand for high security of personal identification system also raised. Although fingerprint, face recognition and other biometric system have been used widely, they have shortages of liveness detection, anti-spoofing, anti-forcing, etc.. In this paper, we studied the application of Electroencephalography (EEG) signal in personal identification. EEG is a validated effective biometric modality with distinct advantages. First, the active EEG must come from a living individual with a normal mental state. Second, EEG is hard to mimic. Finally, nowadays, advances in EEG recoding hardware dramatically simplified EEG acquisition procedure, and reduced the cost. In the past five years, EEG gradually emerged as a promising biometric modality to enhance the anti-spoofing capability of the existing biometric systems, and demonstrated some unique advantages in applications with high security requirements.
In the second part of this thesis, pupil location in infrared video is realized. The goal is to locate the centre of pupil in infrared video, and it is of great value in practical applications. We present the location of pupil to BPPV (Benign Paroxysmal Positional Vertigo) diagnosis and treatment system. The system has been used in some hospitals now.
The main tasks of this thesis include two parts:EEG-based personal identification and pupil location in infrared video. The details are as follows:
(1) AR model parameters and power spectrum density are common features of EEG-based personal identification system. In this paper, we apply feature selection to the new EEG database, and extract Hilbert-Huang spectrum from EEG signal. What is more, the sparse representation-based classification to EEG-based personal identification system is also addressed.
(2) We build an open-set personal identification system based on SVM. It solves the problem that k-NN classify cannot be applied to open-set situation. Experimental results show that EER is low and the accuracy is almost the same as k-NN classify on the same database.
(3) Two dual-biometric-modality identification systems are implemented in this thesis. Fusion system makes advantages of fingerprint and EEG complementary to each other, the system of high accuracy and anti-spoofing, the performance of security was improved greatly.
(4) Muscle signals always are treated as noise in EEG. However, we used muscle signals as a robust covert warning message novelty. Two algorithms based on time domain and Hilbert-Huang spectrum respectively are proposed, where both can detect covert warning messages perfectly. Experimental results verify the feasibility of EEG-based covert warning system.
(5) A real time pupil location system is implemented with high accuracy, and balances the accuracy and efficiency perfectly. The median filter arithmetic, Hough transform circle detection arithmetic and Daugman's integro-differential operator are improved to make the system efficient on the condition of high accuracy.
红外视频瞳孔定位系统是计算机视觉方面的应用项目,该项目的目标是准确地定位红外视频中瞳孔的中心位置。红外视频瞳孔定位具有广泛的实际应用,本文的定位结果用于良性阵发性位置性眩晕诊断治疗系统,本系统已经在部分医院的设备中得到了应用。
本文的工作分为两个部分：脑电波身份识别和红外视频瞳孔定位。主要内容包括：
(1)在脑电信号特征提取和分类器方面,对AR系数特征和功率谱密度特征在新的数据集上做特征选择,使用希尔伯特谱作为新的特征；将基于稀疏表示的分类器引入到基于脑电信号的身份识别系统中；
(2)构建了基于SVM的开集身份识别系统,弥补了k-NN分类器不能用于开集的不足。实验结果表明基于SVM的开集身份识别系统具有较低的EER,并且识别准确率也与同样数据下的k-NN分类器的识别准确率相当；
(3)实现了两种基于EEG的多模态抗伪造身份识别系统。融合后的系统使得指纹和脑电信号这两种生物特征模态优势互补,系统既具有指纹识别的高准确率,又具有脑电信号的抗伪造性,大大提高了安全性能；
(4)脑电信号中的肌电信号一般都被认为是噪声,本文创新性地利用肌电信号搭建了隐蔽报警系统,实现了基于时域的隐蔽报警检测算法和基于希尔伯特黄变换的隐蔽报警检测算法,两者均能准确地检测脑电信号中的隐蔽报警信号,验证了基于脑电信号的隐蔽报警系统的可行性；
(5)在红外视频瞳孔定位项目中很好地权衡了定位准确率与算法效率,实现了高准确率的实时瞳孔定位系统。改进了中值滤波算法、Hough变换圆检测算法和Daugman圆检测算子,在保证准确率的前提下,降低了计算复杂度。
As biometrics being used more and more widely in our daily life, the demand for high security of personal identification system also raised. Although fingerprint, face recognition and other biometric system have been used widely, they have shortages of liveness detection, anti-spoofing, anti-forcing, etc.. In this paper, we studied the application of Electroencephalography (EEG) signal in personal identification. EEG is a validated effective biometric modality with distinct advantages. First, the active EEG must come from a living individual with a normal mental state. Second, EEG is hard to mimic. Finally, nowadays, advances in EEG recoding hardware dramatically simplified EEG acquisition procedure, and reduced the cost. In the past five years, EEG gradually emerged as a promising biometric modality to enhance the anti-spoofing capability of the existing biometric systems, and demonstrated some unique advantages in applications with high security requirements.
In the second part of this thesis, pupil location in infrared video is realized. The goal is to locate the centre of pupil in infrared video, and it is of great value in practical applications. We present the location of pupil to BPPV (Benign Paroxysmal Positional Vertigo) diagnosis and treatment system. The system has been used in some hospitals now.
The main tasks of this thesis include two parts:EEG-based personal identification and pupil location in infrared video. The details are as follows:
(1) AR model parameters and power spectrum density are common features of EEG-based personal identification system. In this paper, we apply feature selection to the new EEG database, and extract Hilbert-Huang spectrum from EEG signal. What is more, the sparse representation-based classification to EEG-based personal identification system is also addressed.
(2) We build an open-set personal identification system based on SVM. It solves the problem that k-NN classify cannot be applied to open-set situation. Experimental results show that EER is low and the accuracy is almost the same as k-NN classify on the same database.
(3) Two dual-biometric-modality identification systems are implemented in this thesis. Fusion system makes advantages of fingerprint and EEG complementary to each other, the system of high accuracy and anti-spoofing, the performance of security was improved greatly.
(4) Muscle signals always are treated as noise in EEG. However, we used muscle signals as a robust covert warning message novelty. Two algorithms based on time domain and Hilbert-Huang spectrum respectively are proposed, where both can detect covert warning messages perfectly. Experimental results verify the feasibility of EEG-based covert warning system.
(5) A real time pupil location system is implemented with high accuracy, and balances the accuracy and efficiency perfectly. The median filter arithmetic, Hough transform circle detection arithmetic and Daugman's integro-differential operator are improved to make the system efficient on the condition of high accuracy.
引文
[1]Anil K. Jain, Patrick Flynn, Arun A. Ross, Handbook of Biometrics, Springer, 2007.
[2]A. K. Jain, A. Ross, and S. Prabhakar. An Introduction to Biometric Recognition. IEEE Transactions on Circuits and Systems for Video Technology, Special Issue on Image-and Video-Based Biometrics, January 2004.
[3]A. K. Jain, R. Bolle, and S. Pankanti, editors. Biometrics:Personal Identification in Networked Society. Kluwer Academic Publishers,1999.
[4]http://vast.uccs.edu/-tboult/#announce
[5]Researchers Hack Faces in Biometric Facial Authentication Systems, http://www.darkreading.com/security/vulnerabilities/213901113/index.html.
[6]S. Sanei, J. A. Chambers, EEG Signal Processing, John Wiley & Sons Ltd, England,2007.
[7]Poulos M., Rangoussi M.,Neural network based person identification using EEG features,IEEE international conference on acoustics, speech, and signal processing,1999
[8]Poulos M., Rangoussi M., Chrissikopoulos V., Evangelou A., Person identification based on parametric processing of the EEG, The 6th IEEE International Conference on Electronics, Circuits and Systems,1999
[9]S. Fei, X. Liwen, C. Anni, et al., EEG-based personal identification:from proof-of-concept to a practical system,20th International Conference on Pattern Recognition,2010, pp.3728-3731.
[10]S. Fei, X. Liwen, C. Anni, et al., Evaluation of recording factors in EEG-based personal identification:A vital step in real implementations, IEEE International Conference on Systems Man and Cybernetics,2010, pp.3861-3866.
[11]S. Fei, X. Liwen, C. Anni, et al., A dual-biometric-modality identification system based on fingerprint and EEG, Fourth IEEE International Conference on Biometrics:Theory Applications and Systems,2010, pp.1-6.
[12]http://cs229.stanford.edu/
[13]http://en.wikipedia.org/wiki/Electroencephalography
[14]http://en.wikipedia.org/wiki/Autoregressive_model
[15]Norden E. Huang, Zheng Shen, Steven R. Long, Manli C. Wu, Hsing H. Shih, Quanan Zheng, Nai-Chyuan Yen, Chi Chao Tung and Henry H. Liu, "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis", Proc. R. Soc. Lond. A.1998:903-1005P
[16]Wright.J., Yang. A.Y., Ganesh. A., Sastry. S.S., Yi Ma, "Robust Face Recognition via Sparse Representation", IEEE Trans. Pattern Analysis and Machine Intelligence, vol.31,2009, pp.210-227.
[17]A. K. Jain, Biometric system security, http://biometrics.cse.msu.edu,2006.
[18]V. Valencia, "Biometric liveness testing, in biometrics", J. D. Woodward, Ed. New York,2002, Chapter 8.
[19]S. A. C. Schuckers, "Spoofing and anti-spoofing measures," Information Security Technical Report,2002, vol.7, no.4, pp.56-62.
[20]D. Martin, D. Lodrova, "Liveness detection for biometric systems based on papillary lines," Int. J. Security and Its Applications, vol.2, no.4, Oct.2008.
[21]龙林,基于多模态生物特征的身份识别,2005
[22]Jain A.K., Ross A.A., Pankanti S., "A Prototype Hand Geometry-based Verification System", International Proceedings on Audio & Video-Based Biometric Person Identification, pp.166-171,1999.
[23]Korotkaya Z., "Biometric Person Authentication:Odor", Available online: http://www.it.lut.fi/kurssit/03-04/010970000/seminars/Korotkaya.pdf,2003.
[24]Jain A.K., "Biometric System Security," http://biometrics.cse.msu.edu,2006.
[25]Bowyer K.W., Chang K.I., et al., "Multi-modal biometrics:an overview", In Second Workshop on Multi-Modal User Authentication,2006.
[26]http://vast.uccs.edu/-tboult/#announce
[27]Researcher's Hack Faces in Biometric Facial Authentication Systems, http://www. darkreading.com/security/vulnerabilities/showArticle.jhtml?articleID=213901113
[28]A. K. Jain, L. Hong, R. Bolle, "On-line fingerprint verification," IEEE Trans. Pattern Analysis and Machine Intelligence, vol.19, no.4,1997.
[29]F. Su, X.-H. Xie, J.-J. Feng, A. Cai, "Fingerprint matching using SVM-based similarity measure," Chinese J. Electronics, vol.16,2007, Part 3, pp.459-463.
[30]J.-J. Feng, "Combining minutiae descriptors for fingerprint matching," Pattern Recognition, vol.41,2008, pp.342-352.
[31]夏立文,基于脑电波信号的身份识别技术,北京邮电大学硕士学位论文,2010
[32]D. Li, D. Winfield, D. J. Parkhurst, Starburst:A hybrid algorithm for video-based eye tracking combining feature-based and model-based approaches, CVPR,2005
[33]L Deng; X Xiong; J Zhou; P Gan; S Deng; Fatigue detection based on infrared video pupillography, iCBBE,2010
[34]宋伟,鲁宏华,陈人生,张文萍,马元煦,震动眼震试验在眩晕诊断中的应用,Tianjin, Med, J, Feb 2008, Vol 36 No.2
[35]阮秋琦,阮宇智等译,数字图像处理,电子工业出版社
[36]http://www.srmtec.com/
[37]S. Perreault, P. Hebert, Median Filtering in Constant Time, IEEE Transactions on Image Processing, Vol 16(9),2007, pp.2389-2394
[38]T. Huang, G. Yang, and G. Tang, "A fast two-dimensional median filtering algorithm," IEEE Trans. Acoust., Speech, Signal Process., vol.27, no.2, pp. 13-18, Feb.1979.
[39]R. Hough. Methods and Means for Recognizing Complex Patterns. USPatent306965
[40]于海滨,基于头部特征提取的人体检测与跟踪及其应用,2007
[41]Daugman J D, High confidence visual recognition of persons by a test of statistical independence [J].IEEE,1993,15(11):1148-1161.
[42]杨秋芬,桂卫华,胡豁生,周书仁,驾驶员疲劳驾驶中的眼睛定位创新算法,计算机工程与应用,2008年第6期
[43]袁小庆等,眼震检测中眼动曲线拟合方法研究,计算机仿真,2009年2期
[44]谷君,机器学习算法在基于语义图像检索中的应用,北京交通大学学位论文,2009
[45]姜伟等,基于边缘背景差法和Hough变换的公交乘客头部检测方法研究,青岛大学学报：工程技术版,2010年2期
[46]孙亦南等,一种用于圆检测的改进Hough变换方法,计算机工程与应用,2003年20期
[47]王思国,基于机器视觉的黑片缺陷检测定位技术研究,沈阳理工大学学位论文,2009
[2]A. K. Jain, A. Ross, and S. Prabhakar. An Introduction to Biometric Recognition. IEEE Transactions on Circuits and Systems for Video Technology, Special Issue on Image-and Video-Based Biometrics, January 2004.
[3]A. K. Jain, R. Bolle, and S. Pankanti, editors. Biometrics:Personal Identification in Networked Society. Kluwer Academic Publishers,1999.
[4]http://vast.uccs.edu/-tboult/#announce
[5]Researchers Hack Faces in Biometric Facial Authentication Systems, http://www.darkreading.com/security/vulnerabilities/213901113/index.html.
[6]S. Sanei, J. A. Chambers, EEG Signal Processing, John Wiley & Sons Ltd, England,2007.
[7]Poulos M., Rangoussi M.,Neural network based person identification using EEG features,IEEE international conference on acoustics, speech, and signal processing,1999
[8]Poulos M., Rangoussi M., Chrissikopoulos V., Evangelou A., Person identification based on parametric processing of the EEG, The 6th IEEE International Conference on Electronics, Circuits and Systems,1999
[9]S. Fei, X. Liwen, C. Anni, et al., EEG-based personal identification:from proof-of-concept to a practical system,20th International Conference on Pattern Recognition,2010, pp.3728-3731.
[10]S. Fei, X. Liwen, C. Anni, et al., Evaluation of recording factors in EEG-based personal identification:A vital step in real implementations, IEEE International Conference on Systems Man and Cybernetics,2010, pp.3861-3866.
[11]S. Fei, X. Liwen, C. Anni, et al., A dual-biometric-modality identification system based on fingerprint and EEG, Fourth IEEE International Conference on Biometrics:Theory Applications and Systems,2010, pp.1-6.
[12]http://cs229.stanford.edu/
[13]http://en.wikipedia.org/wiki/Electroencephalography
[14]http://en.wikipedia.org/wiki/Autoregressive_model
[15]Norden E. Huang, Zheng Shen, Steven R. Long, Manli C. Wu, Hsing H. Shih, Quanan Zheng, Nai-Chyuan Yen, Chi Chao Tung and Henry H. Liu, "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis", Proc. R. Soc. Lond. A.1998:903-1005P
[16]Wright.J., Yang. A.Y., Ganesh. A., Sastry. S.S., Yi Ma, "Robust Face Recognition via Sparse Representation", IEEE Trans. Pattern Analysis and Machine Intelligence, vol.31,2009, pp.210-227.
[17]A. K. Jain, Biometric system security, http://biometrics.cse.msu.edu,2006.
[18]V. Valencia, "Biometric liveness testing, in biometrics", J. D. Woodward, Ed. New York,2002, Chapter 8.
[19]S. A. C. Schuckers, "Spoofing and anti-spoofing measures," Information Security Technical Report,2002, vol.7, no.4, pp.56-62.
[20]D. Martin, D. Lodrova, "Liveness detection for biometric systems based on papillary lines," Int. J. Security and Its Applications, vol.2, no.4, Oct.2008.
[21]龙林,基于多模态生物特征的身份识别,2005
[22]Jain A.K., Ross A.A., Pankanti S., "A Prototype Hand Geometry-based Verification System", International Proceedings on Audio & Video-Based Biometric Person Identification, pp.166-171,1999.
[23]Korotkaya Z., "Biometric Person Authentication:Odor", Available online: http://www.it.lut.fi/kurssit/03-04/010970000/seminars/Korotkaya.pdf,2003.
[24]Jain A.K., "Biometric System Security," http://biometrics.cse.msu.edu,2006.
[25]Bowyer K.W., Chang K.I., et al., "Multi-modal biometrics:an overview", In Second Workshop on Multi-Modal User Authentication,2006.
[26]http://vast.uccs.edu/-tboult/#announce
[27]Researcher's Hack Faces in Biometric Facial Authentication Systems, http://www. darkreading.com/security/vulnerabilities/showArticle.jhtml?articleID=213901113
[28]A. K. Jain, L. Hong, R. Bolle, "On-line fingerprint verification," IEEE Trans. Pattern Analysis and Machine Intelligence, vol.19, no.4,1997.
[29]F. Su, X.-H. Xie, J.-J. Feng, A. Cai, "Fingerprint matching using SVM-based similarity measure," Chinese J. Electronics, vol.16,2007, Part 3, pp.459-463.
[30]J.-J. Feng, "Combining minutiae descriptors for fingerprint matching," Pattern Recognition, vol.41,2008, pp.342-352.
[31]夏立文,基于脑电波信号的身份识别技术,北京邮电大学硕士学位论文,2010
[32]D. Li, D. Winfield, D. J. Parkhurst, Starburst:A hybrid algorithm for video-based eye tracking combining feature-based and model-based approaches, CVPR,2005
[33]L Deng; X Xiong; J Zhou; P Gan; S Deng; Fatigue detection based on infrared video pupillography, iCBBE,2010
[34]宋伟,鲁宏华,陈人生,张文萍,马元煦,震动眼震试验在眩晕诊断中的应用,Tianjin, Med, J, Feb 2008, Vol 36 No.2
[35]阮秋琦,阮宇智等译,数字图像处理,电子工业出版社
[36]http://www.srmtec.com/
[37]S. Perreault, P. Hebert, Median Filtering in Constant Time, IEEE Transactions on Image Processing, Vol 16(9),2007, pp.2389-2394
[38]T. Huang, G. Yang, and G. Tang, "A fast two-dimensional median filtering algorithm," IEEE Trans. Acoust., Speech, Signal Process., vol.27, no.2, pp. 13-18, Feb.1979.
[39]R. Hough. Methods and Means for Recognizing Complex Patterns. USPatent306965
[40]于海滨,基于头部特征提取的人体检测与跟踪及其应用,2007
[41]Daugman J D, High confidence visual recognition of persons by a test of statistical independence [J].IEEE,1993,15(11):1148-1161.
[42]杨秋芬,桂卫华,胡豁生,周书仁,驾驶员疲劳驾驶中的眼睛定位创新算法,计算机工程与应用,2008年第6期
[43]袁小庆等,眼震检测中眼动曲线拟合方法研究,计算机仿真,2009年2期
[44]谷君,机器学习算法在基于语义图像检索中的应用,北京交通大学学位论文,2009
[45]姜伟等,基于边缘背景差法和Hough变换的公交乘客头部检测方法研究,青岛大学学报：工程技术版,2010年2期
[46]孙亦南等,一种用于圆检测的改进Hough变换方法,计算机工程与应用,2003年20期
[47]王思国,基于机器视觉的黑片缺陷检测定位技术研究,沈阳理工大学学位论文,2009