一种Gabor小波和K-L变换的掌纹识别方法研究
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摘要
生物识别技术(Biometric)是利用人体所固有的生物特征来进行自动身份识别的技术。人体生物特征包括物理特征(如指纹、虹膜、脸型、掌纹等)和行为特征(如步态、签名等)等,它具有普遍性、唯一性和稳定性等特点,并且不会被遗忘,也较难被模仿或伪造,从而可以取代或加强传统的身份识别方法。随着国家安全、金融证券、电子商务等领域安全性需求的逐年增强,新型的身份识别技术变得越来越重要,生物特征识别将为此提供有效的解决方案。
掌纹识别是利用人的掌部纹理作为生物特征进行身份的自动确认,与常见的指纹、人脸、虹膜等生物特征相比,以其信息量丰富、特征稳定和易于采集的特点而作为一种很具吸引力和发展潜力的生物特征识别技术。但目前国内外对掌纹的研究还远没有象指纹、人脸、说话人识别的研究那样广泛和深入。本论文针对掌纹的具体特点,对掌纹识别系统中的关键技术与核心算法进行了较深入的研究。
文章首先简要介绍了掌纹识别技术的发展和研究现状,然后详细讨论了掌纹识别系统的基本组成以及各个模块的关键技术和发展现状,其中重点研究了掌纹识别算法,对近年来出现的主流方法进行深入的研究,归纳出现有文献中涉及掌纹特征提取的4类算法。具体分析了Gabor小波变换、K-L变换、Fisher线性判别式和包含在类平均图像中判别信息的最优压缩方法在掌纹特征提取中的应用,然后根据各方法的优缺点,将基于时频变换的特征提取算法和基于子空间的特征提取算法结合起来,提出了利用Gabor小波和原空间判别信息K-L变换实现掌纹特征提取的方法。该算法先对掌纹预处理后的灰度图象进行Gabor小波变换,得到掌纹的Gabor特征向量,然后利用原空间判别信息K-L变换的方法将高维特征向量变换到低维空间。该算法既充分利用了Gabor函数优良的特征提取性能,又有效地解决了高维特征的降维处理问题。通过在香港理工大学生物识别研究中心提供的掌纹数据库(the PolyU Palmprint Database)及自行采集建立的私有库实验证明,该算法有较好的鲁棒性和有效性,可以较好地实现掌纹的特征提取和识别。
Biometrics is the technology that uses human body characteristics for automated personal authentication. Personal characteristics including physiological characteristics (such as fingerprint, iris, face, palmprint) and behavioral characteristics (such as pace, signature) are universal, unique and permanent, they are also unforgettable and hard to imitate and forge, so that they can be used to reinforce or even replace the traditional authentication tools. Biometric technology will be widely used in national security system, financial system, e-commerce and other related field.
Palmprint recognition technology makes use of human palm texture as biometric features to identify human identity. Comparing with other biometric features such as fingerprint, face and iris, palmprint recognition has become a very attractive and best development potential biometric technology by its’abundant information, stable features and easy collection. But, nowadays, the research of palmprint recognition at home and abroad is not as extensive and deep as that of fingerprint recognition, face recognition and speaker identification. According to the detail characteristics of palmprint, this paper makes a deep research to the key technology and core algorithms in palmprint recognition system.
The current developmental situation on palmprint recognition is briefly introduced in the paper, and then each element in palmprint recognition system and its’key technology is discussed in detail, in this part, Arithmetics mainly used in palmprint recognition in the recent years are studied detailly. In this way, four kinds of arithmetics used in palmprint feature extraction in exitent literatures are summarized. The paper puts emphasis on analysising the following feature extraction methods, Gabor wavelet transform, K-L transform, Fisher linear discriminant, generalized K-L transform, then according to their advantages and disadvantages, banding together the feature extraction method based on time to frequency field transform and the feature extraction method based on subspace, a novel algorithm of palmprint feature extraction using Gabor wavelet and original space discreption information K-L transform is put forward. The arithmetic makes use of Gabor transform to obtain Gabor eigenvector of palmprint from palmprint image that has been processed, and then the original space discreption information K-L transform is used to extract the master components and reduce dimensions of Gabor features. The arithmetic makes full use of Gabor function’s fine capability of feature extraction and also effectively solves the problem of feature’s high dimensions, by experimenting in the PolyU Palmprint Database offered by Hongkong Polytechnic University and self-captured palmprint database, it could be proved that the proposed method has good robust and effectiveness, and could well realized palmprint feature extraction.
掌纹识别是利用人的掌部纹理作为生物特征进行身份的自动确认,与常见的指纹、人脸、虹膜等生物特征相比,以其信息量丰富、特征稳定和易于采集的特点而作为一种很具吸引力和发展潜力的生物特征识别技术。但目前国内外对掌纹的研究还远没有象指纹、人脸、说话人识别的研究那样广泛和深入。本论文针对掌纹的具体特点,对掌纹识别系统中的关键技术与核心算法进行了较深入的研究。
文章首先简要介绍了掌纹识别技术的发展和研究现状,然后详细讨论了掌纹识别系统的基本组成以及各个模块的关键技术和发展现状,其中重点研究了掌纹识别算法,对近年来出现的主流方法进行深入的研究,归纳出现有文献中涉及掌纹特征提取的4类算法。具体分析了Gabor小波变换、K-L变换、Fisher线性判别式和包含在类平均图像中判别信息的最优压缩方法在掌纹特征提取中的应用,然后根据各方法的优缺点,将基于时频变换的特征提取算法和基于子空间的特征提取算法结合起来,提出了利用Gabor小波和原空间判别信息K-L变换实现掌纹特征提取的方法。该算法先对掌纹预处理后的灰度图象进行Gabor小波变换,得到掌纹的Gabor特征向量,然后利用原空间判别信息K-L变换的方法将高维特征向量变换到低维空间。该算法既充分利用了Gabor函数优良的特征提取性能,又有效地解决了高维特征的降维处理问题。通过在香港理工大学生物识别研究中心提供的掌纹数据库(the PolyU Palmprint Database)及自行采集建立的私有库实验证明,该算法有较好的鲁棒性和有效性,可以较好地实现掌纹的特征提取和识别。
Biometrics is the technology that uses human body characteristics for automated personal authentication. Personal characteristics including physiological characteristics (such as fingerprint, iris, face, palmprint) and behavioral characteristics (such as pace, signature) are universal, unique and permanent, they are also unforgettable and hard to imitate and forge, so that they can be used to reinforce or even replace the traditional authentication tools. Biometric technology will be widely used in national security system, financial system, e-commerce and other related field.
Palmprint recognition technology makes use of human palm texture as biometric features to identify human identity. Comparing with other biometric features such as fingerprint, face and iris, palmprint recognition has become a very attractive and best development potential biometric technology by its’abundant information, stable features and easy collection. But, nowadays, the research of palmprint recognition at home and abroad is not as extensive and deep as that of fingerprint recognition, face recognition and speaker identification. According to the detail characteristics of palmprint, this paper makes a deep research to the key technology and core algorithms in palmprint recognition system.
The current developmental situation on palmprint recognition is briefly introduced in the paper, and then each element in palmprint recognition system and its’key technology is discussed in detail, in this part, Arithmetics mainly used in palmprint recognition in the recent years are studied detailly. In this way, four kinds of arithmetics used in palmprint feature extraction in exitent literatures are summarized. The paper puts emphasis on analysising the following feature extraction methods, Gabor wavelet transform, K-L transform, Fisher linear discriminant, generalized K-L transform, then according to their advantages and disadvantages, banding together the feature extraction method based on time to frequency field transform and the feature extraction method based on subspace, a novel algorithm of palmprint feature extraction using Gabor wavelet and original space discreption information K-L transform is put forward. The arithmetic makes use of Gabor transform to obtain Gabor eigenvector of palmprint from palmprint image that has been processed, and then the original space discreption information K-L transform is used to extract the master components and reduce dimensions of Gabor features. The arithmetic makes full use of Gabor function’s fine capability of feature extraction and also effectively solves the problem of feature’s high dimensions, by experimenting in the PolyU Palmprint Database offered by Hongkong Polytechnic University and self-captured palmprint database, it could be proved that the proposed method has good robust and effectiveness, and could well realized palmprint feature extraction.
引文
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[2] Shah S, Sastry P S. Fingerprint classification using a feedback-based line detector [J]. IEEE Transactions on Systems, Man and Cybernetics, Part B. Vol.34, Issue: 1, 2004, 85-94.
[3] Chul-Hyun Park, Joon-Jae Lee, Smith M J T, et al. Directional filter bank-based fingerprint feature extraction and matching [J]. IEEE Transaction on Circuits and Systems for Video Technology, Vol.14, Issue: 1, Jan 2004, 74-85.
[4] Zalev J, Sedaghat R. Automatic fingterprint recognition algorithm [C].Electrical and Computer Engineering, Canadian Conference, Vol. 4, May 2004, 2263-2266.
[5] Shenglin Yang, Verbauwhede I. A realtime, memory efficient fingerprint verification system [A]. Acoustics, Speech, and Signal Processing Proceedings IEEE International Conference [C]. Vol.5, May 2004. 189-192.
[6] Keun-Chang Kwak, Pedrycz W. Face recognition using fuzzy Intergral and wavelet decomposition method [J]. Systems, Man and Cybernetics, Part B, IEEE Transactions. Aug. 2004, 34(4):1666-1675.
[7] Chengjun Liu. Gabor-based kernel PCA with fractional power polynomial models for face recognition [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, May 2004, 26(5): 572-581.
[8] Qingshan Liu, Hanqing Lu, Songde Ma. Improving kernel fisher discriminant analysis for face recognition [J], IEEE Transactions on Circuits and Systems for Video Technology. Jan. 2004, 14(1): 42-49.
[9] Krichen E, Mellakh M A, Garcia-Salicetti S, et al. Iris identification using wavelet packets. Pattern Recognition-Proceedings of the 17th International Conference [C]. Vol.4, Aug 2004, 335-338.
[10] Junzhou Huang, Yunhong Wang, Tieniu Tan, et al. A new iris segmentation method for recognitiong [A]. Pattern Recognition—Proceedings of the 17th International Conference [C]. Vol.3, Aug 2004. 554-557.
[11] R B. Hill. Apparatus and Method for Identifying Individuals through Their Retinal Vasculature Patterns [P]. US Patent. 1978.
[12] Newham, Emma. Signature Verification Technologties [A]. Biometic Technology Today. 2000, 8(4): 8-11.
[13] Anile K. Jain, Friederike D. Griess, Scott D. Connell. On-line signature verification [J]. Pattern Recognition. 2002, 35(12): 2963-2972.
[14] Wei Shu, Zhang D. Palmprint verification: an implementation of biometric technology[C].Pattern Recognition Proceedings of Fourteenth International Conference, 1998(1):219-221.
[15] A. Kumar and H.Shen. Recognition of palmprints using wavelet-based features [C]. Processings of International Conference on System, Cybematic, SCI-2002. Orlando, Florida, USA, 2002.
[16] .C. C. Han, H. L. Cheng, C. L. Lin, K. C. Fan. Personal Authetication Using Palmprint Features [J]. Pattern Recognition. 2003, 36: 371-381.
[17] Li Wenxin, Zhang David, Xu Zhouqun. Image Alignment Based on Invariant Features for Identification [J]. Signal Processing Image Communication, 2003(18): 373-379.
[18]黎明,严超华,刘高航.基于掌纹图象分析的身份识别系统[J].中国图象图形学报, 2005(2):134-137.
[19] Tee Connie, Andrew Teoh, Michael Goh and David Ngo. PalmHashing: A Novel Approach for Dual Factor Authentication [J]. Pattern Analysis and Applications Springer-Verlag, 2003, 7(3):255-268.
[20] Duta N, Jain A.K, Mardia K.V. Matching of Palmprint [J]. Pattern Recognition Letters, 2001, 23(4): 477-485.
[21] Li Wenxin, Zhang David. Palmprint Identification by Fourier Transform [J]. International Journal of Pattern Recognition and Artificial Intelligence, 2002, 16(4): 417-432.
[22] .苏晓生,林喜荣,丁天怀等.基于小波变换的掌纹的特征捉取[J],清华大学学报, 2003, 43(8):1049-1051.
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