近红外手背静脉识别算法研究
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摘要
人体静脉识别是一项新的非接触式生物特征识别技术,它在安全保护和身份认证领域都有广泛用途。静脉识别的发展开始于2000年前后,系统开发还不够成熟和完善,但是这些都不妨碍研究人员对其的浓厚兴趣。凭借着人类活体静脉独一无二、安全可靠的特性,杜绝了造假或剽窃的可能性。而且在商业化实践的过程中,我们还发现的其相对于其他生物特征识别技术而言,工作稳定,使用便捷舒适的优点,所有这些都更激发了人们探索和完善这项技术的渴望。
和人脸识别、指纹识别等其他生物识别技术相似,人体静脉识别技术在商用系统实现的同样面临着许多有待解决的问题。比如现有的静脉识别研究中的静脉图像受环境光照影响问题、算法计算复杂度过高等,因此并不能很好的应用到实际静脉识别产品的开发中。
针对现有的问题和局限,本文在以下方面进行了探索,主要贡献为：
1.基于分水岭和豪斯道夫距离的手背静脉识别算法
该算法采用了分水岭算法直接提取静脉的单像素级骨架特征,利用静脉骨架的端点和交叉点作为静脉模式的特征,并且使用了改进的豪斯道夫距离来作为静脉特征匹配识别算法,并且在自制静脉图库上验证了该新算法的性能和特点。这种算法避免了传统静脉特征提取算法细化骨架特征时带来毛刺和噪声的问题。
2.基于Gabor相位编码的手背静脉识别算法
该算法分析了现有基于静脉骨架特征的算法对于表达整个手背静脉特征的局限性,提出了基于Gabor相位编码的识别方法。算法利用二维Gabor滤波器提取了静脉图像的相位信息,然后将编码后的相位信息作为静脉特征,并且利用海明距离作为特征匹配识别算法。在不考虑静脉图像样本几何变化的情况下,相对于现有的静脉骨架特征算法取得了很好的识别效果。
3.基于局部Gabor相位信息的手背静脉识别算法
在Gabor相位编码算法的基础之上,局部Gabor相位信息算法,将Gabor相位编码的局部特征和全局特征结合起来,采用局部异或算子提取相位编码的局部变化,再利用局部变化特征值的直方图信息作为整个静脉特征,使静脉特征模式更加丰富。并且利用卡方距离作为特征匹配识别的算法。实验表明,本算法对图像的几何变化有很好的鲁棒性,提高了静脉识别系统的性能。
Vein pattern recognition for human body is a newly coming touch-less biometrics, which holds great applicable vision on security and identification. The research of vein authentication technology started from around the year thousand and seemed to be very appealing to all the researchers although the developing system is not sophisticated.Compared to other kind of biometric authentication technology, it has lots of advantages.Human vein cannot be forged or stolen since its uniqueness and stability. And the hand vein authentication system is also easy and comfortable for people to use.That is the reason why people are putting much more energy and expectation into the research work to promote this technology.
Comparing to the face recognition, fingerprint recognition and other biometrics, vein pattern recognition is also facing many problems in consumer system implementation.As there are still some problems such as environment lighting, algorithm complexity etc, vein recognition is really hard to be used in biometric security system.
Based on the problems mentioned above, this article has made researches on the following aspects:
1.Palm-dorsal vein Biometrics based on watershed algorithm
This approach is based on watershed algorithm to detect the single pixel level skeleton of the hand vein.Using the ending point and the crossing point of the extracted vein skeleton as the feature point, then measure the similarity between the registered vein image and the sampled vein image using Hausdorff distance. Experiments were conducted on our own palm-dorsal vein image database and the results are satisfactory. This approach avoids the noise problem and glitch problem happened in the thinning stage in traditional vein image recognition.
2.Palm-dorsal vein recognition based on Gabor phase encoding method
Firstly, this part of the article discusses the shortage of the traditional vein image recognition based on the vein skeleton feature and then presents the vein image recognition based on Gabor phase encoding approach.The approach uses 2D Gabor filter phase encoding to represent the texture feature of the vein image and uses Hamming distance to evaluate the matching degree.Without considering of the geometric transformation, experimental results on our own palm-dorsal vein image database indicates the vein pattern biometric is potentially a useful biometric.
3.Local Gabor phase feature for Palm-dorsal vein recognition
This part of the article presents the local Gabor phase feature for vein recognition approach based on the Gabor encoding method discussed in last section. The approach combined the local Gabor feature with the global feature by using a local XOR pattern operator to extract the local variance feature of the Gabor phase encoding and using histogram method to represent the global feature.Chi-square distance is applied to verify the efficiency of our method, experiments were carried on our own palm-dorsal vein image database.The experimental results show that the local Gabor phase feature could provide sufficient information for vein recognition and robust to geometric transformation.
和人脸识别、指纹识别等其他生物识别技术相似,人体静脉识别技术在商用系统实现的同样面临着许多有待解决的问题。比如现有的静脉识别研究中的静脉图像受环境光照影响问题、算法计算复杂度过高等,因此并不能很好的应用到实际静脉识别产品的开发中。
针对现有的问题和局限,本文在以下方面进行了探索,主要贡献为：
1.基于分水岭和豪斯道夫距离的手背静脉识别算法
该算法采用了分水岭算法直接提取静脉的单像素级骨架特征,利用静脉骨架的端点和交叉点作为静脉模式的特征,并且使用了改进的豪斯道夫距离来作为静脉特征匹配识别算法,并且在自制静脉图库上验证了该新算法的性能和特点。这种算法避免了传统静脉特征提取算法细化骨架特征时带来毛刺和噪声的问题。
2.基于Gabor相位编码的手背静脉识别算法
该算法分析了现有基于静脉骨架特征的算法对于表达整个手背静脉特征的局限性,提出了基于Gabor相位编码的识别方法。算法利用二维Gabor滤波器提取了静脉图像的相位信息,然后将编码后的相位信息作为静脉特征,并且利用海明距离作为特征匹配识别算法。在不考虑静脉图像样本几何变化的情况下,相对于现有的静脉骨架特征算法取得了很好的识别效果。
3.基于局部Gabor相位信息的手背静脉识别算法
在Gabor相位编码算法的基础之上,局部Gabor相位信息算法,将Gabor相位编码的局部特征和全局特征结合起来,采用局部异或算子提取相位编码的局部变化,再利用局部变化特征值的直方图信息作为整个静脉特征,使静脉特征模式更加丰富。并且利用卡方距离作为特征匹配识别的算法。实验表明,本算法对图像的几何变化有很好的鲁棒性,提高了静脉识别系统的性能。
Vein pattern recognition for human body is a newly coming touch-less biometrics, which holds great applicable vision on security and identification. The research of vein authentication technology started from around the year thousand and seemed to be very appealing to all the researchers although the developing system is not sophisticated.Compared to other kind of biometric authentication technology, it has lots of advantages.Human vein cannot be forged or stolen since its uniqueness and stability. And the hand vein authentication system is also easy and comfortable for people to use.That is the reason why people are putting much more energy and expectation into the research work to promote this technology.
Comparing to the face recognition, fingerprint recognition and other biometrics, vein pattern recognition is also facing many problems in consumer system implementation.As there are still some problems such as environment lighting, algorithm complexity etc, vein recognition is really hard to be used in biometric security system.
Based on the problems mentioned above, this article has made researches on the following aspects:
1.Palm-dorsal vein Biometrics based on watershed algorithm
This approach is based on watershed algorithm to detect the single pixel level skeleton of the hand vein.Using the ending point and the crossing point of the extracted vein skeleton as the feature point, then measure the similarity between the registered vein image and the sampled vein image using Hausdorff distance. Experiments were conducted on our own palm-dorsal vein image database and the results are satisfactory. This approach avoids the noise problem and glitch problem happened in the thinning stage in traditional vein image recognition.
2.Palm-dorsal vein recognition based on Gabor phase encoding method
Firstly, this part of the article discusses the shortage of the traditional vein image recognition based on the vein skeleton feature and then presents the vein image recognition based on Gabor phase encoding approach.The approach uses 2D Gabor filter phase encoding to represent the texture feature of the vein image and uses Hamming distance to evaluate the matching degree.Without considering of the geometric transformation, experimental results on our own palm-dorsal vein image database indicates the vein pattern biometric is potentially a useful biometric.
3.Local Gabor phase feature for Palm-dorsal vein recognition
This part of the article presents the local Gabor phase feature for vein recognition approach based on the Gabor encoding method discussed in last section. The approach combined the local Gabor feature with the global feature by using a local XOR pattern operator to extract the local variance feature of the Gabor phase encoding and using histogram method to represent the global feature.Chi-square distance is applied to verify the efficiency of our method, experiments were carried on our own palm-dorsal vein image database.The experimental results show that the local Gabor phase feature could provide sufficient information for vein recognition and robust to geometric transformation.
引文
[1]王科俊,丁宇航,王大振.基于静脉识别的身份认证方法研究[J].科技导报,2005,35(1)：35-37.
[2]Koichi Shimizu, Optical trans-bloody imaging:feasibility of living body[J]. Japan Journal of Medicinal Philosophica,1992,11:620-629.
[3]M.Naoto, A.Nagasaka, M.Takafumi.Feature extraction of finger vein patterns based on repeated line tracking and its application to personal identification[J]. Systems and Computers in Iapan,2004,35:61-71.
[4]张会林,简献忠.人体手背静脉血管图像增强处理算法研究.仪器仪表学报[J].2005,26(8)：44-49.
[5]Naoto Miura, Akio Nagasaka, Takafumi Miyatake, Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification[J].Machine Vision Application,2004,15(2):194-203.
[6]Toshiyuki Tanaka, Naohiko Kubo, Biometric authentication by hand vein patterns[C].SICE, Annual Conference in Sapporo,2004:249-253.
[7]Shinichiro Watanabe, Toshiyuki Tanaka, Eizaburo Iwata. Vein emphasis filter for Biometric Authentication.SICE Annual Conference,2007:690-693.
[8]Fujitsu Laboratories Ltd. Fujitsu Laboratories develops technology for world's first contact-less palm vein pattern biometric authentication system[EB/OL].http://pr.fuiitsu.com/en/new s/2003/03/31.html.
[9]索尼公司(日本东京).索尼开发出手指静脉识别技术"mofiria". [EB/OL].http://ww w.sony51.com/?article-58.html
[10]Ki-chung Chung, Seok Cheol Kee, Sang Ryong Kim,Face recognition using principal component analysis of Gabor filter responses[C] Proc. Int. Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time system.Greece,1999:53-57.
[11]Sang-Kyun Im, Hyung-Man Park, Kim Young-Woo, et al.An Biometric Identification system by Extracting hand Vein Patterns[J].Journal of the Korean Physical Society.2003, 38(3):268-272.
[12]Sang-Kyun Im, Hyung-Man Park, Soo-Won Kim, Chang-Kyung Chung, Hwan-Soo Choi. Improved Vein Pattern Extraction Algorithm and Its Implementation[J].TUMA.2000(1): 2-3.
[13]Kejun Wang, Yan Zhang, Zhi Yuan and Dayan Zhuang. Hand Vein Recognition Based on Multi Supplemental Features of Multi-Classifier Fusion Decision[C].Proceedings of the 2006 IEEE International Conference on Mechatronics and Automation.2006,1970-1974.
[14]林喜荣,庄波,苏晓生等.人体手背血管图像的特征提取及匹配[J].清华大学学报(自然科学版).2003,43(2)：164-167.
[15]周斌,林喜荣,贾惠波,宋榕.多特征融合的手背血管识别算法[J].清华大学学报(自然科学版).2007,47(2)：194-197.
[16]韩笑,马驷良,张忠波.基于脊波变换的手背静脉图像增强及特征提取[J].吉林大学学报(理学版).2006,44(3)：415-417.
[17]李铁钢,马驷良,张忠波,邱菊.基于Bandelet变换的手背静脉识别算法[J].吉林大 学学报(理学版).2007,45(6)：975-979.
[18]吕佩卓,赖声礼.人体手背静脉血管图像增强与特征匹配[J].计算机工程与设计.2007,28(5)：3623-3627.
[19]吕佩卓,赖声礼,陈佳阳,胡蓉.一种自适应的手背静脉区域定位算法[J].为计算机信息.2008,24(2)：208-210.
[20]张晋阳,孙懋珩.手背静脉图像骨架特征提取的算法[J].计算机应用.2007,27(1)：152-154.
[21]任树梅,王高.一种新颖的静脉生物识别技术设计方案[J].测试技术学报.2004,18：220-222.
[22]裘旭光,张大鹏,刘晶,万振凯.基于自适应区域增长算法获取手部静脉模式.计算机工程与应用.2007,43(33)：230-233.
[23]郭跃衡.揭秘手指静脉识别认证系统[J].中国安防,2007,1：60-61.
[24]崔国范,温学兵.人体静脉识别技术概述[J].中国科技信息期刊(健康与生物医药).2007，16：168.
[25]Chih-Lung Lin and Kuo-Chin Fan. Biometric verification using thermal images of palm dorsa vein patterns[J].PAMI.IEEE,2004,14(2),199-214.
[26]David Zhang, W.-K. Kong, J.You, M.Wong. Online Palm-print Identification [J].IEEE Trans. Pattern Analysis and Machine Intelligence,2003,25(9):1041-1050.
[27]Rafael C.Gonzalez, Richard E.Woods.Digital Image Processing(Second Edition).2003: 25-51.
[28]Kenneth R. Castleman著,朱志刚,林学阎,石定机等泽.数字图像处理,电子工业出版社,2002：94-114.
[29]Chih-Lung Lin Kuo-Chin Fan, Biometric verification using thermal images of palm-dorsa vein patterns [J].IEEE Transaction on Circuits System Video Technology.,2004,14(2): 199-213.
[30]Yunxin Wang, Tiegen Liu, Junfeng Jiang. A multi-resolution wavelet algorithm for hand vein pattern recognition.Chinese Optical Letters.2008,6(9):657-670.
[31]Jian-GangWanga,Wei-Yun Yau, Andy Suwandy, Eric Sung. Person recognition by fusing palm-print and palm vein images based on "Laplacian-palm" representation.Pattern Recognition.2008,41:1514-1527.
[32]Lingyu Wang, Graham Leedham, Siu-Yeung Cho.Infrared imaging of hand vein patterns for biometric purposes[J].IET Computer Vision,2007,1(3-4):113-122.
[33]崔建江,宋星月,陈国坤,陈大力.基于几何形状和小波矩的静脉特征提取与匹配[J].东北大学学报(自然科学版),2009,30(9)：1236-1241.
[34]William J. Rucklidge, Efficiently locating objects using Hausdorff distance[J] International Journal of Computer Vision.1997,24:251-270.
[35]Yongsheng Gao, Maylor K.H.Leung. Line segment Hausdorff distance on face matching[J]. Pattern Recognition.2002,35:361-371.
[36]Lingyu Wang, Graham Leedhamb, David Cho. Minutiae feature analysis for infrared hand vein pattern biometrics [J].Pattern Recognition,2008,41 (5):920-929.
[37]John G. Daugman, High confidence visual recognition of persons by a test of statistical Independence[J],IEEE Trans Pattern Analysis and Machine Intelligence,1993,15(11):1148-1161.
[38]Chengjun Liu, Harry Wechsler. Gabor feature based classifycation using the enhanced fisher discriminant model for face recognition[J].IEEE Trans Image Process.,2002,11(4),467-477.
[39]Dennis Gabor, Theory of communication[J],Journal of Inst. Electronic Engineer.,1946, 93(26):429-457,1946.
[40]Baochang Zhang, Shiguang Shan, Xilin Chen, Wen Gao. Histogram of Gabor Phase Patterns (HGPP):A Novel Object Representation Approach for Face Recognition[J].PAMI IEEE. 2007,61(1):57-58.
[41]Wenchao Zhang, Shiguang Shan, Hongming Zhang, Jie Chen, Xilin Chen, Wen Gao, Histogram sequence of local Gabor binary pattern for face description and identification[J]. Journal of Software 2006,17,2508.
[42]王风华,姚向华,韩九强.基于多通道的Gabor滤波和特征融合的虹膜识别方法[J].光电工程,2007,34(12)：72-77.
[2]Koichi Shimizu, Optical trans-bloody imaging:feasibility of living body[J]. Japan Journal of Medicinal Philosophica,1992,11:620-629.
[3]M.Naoto, A.Nagasaka, M.Takafumi.Feature extraction of finger vein patterns based on repeated line tracking and its application to personal identification[J]. Systems and Computers in Iapan,2004,35:61-71.
[4]张会林,简献忠.人体手背静脉血管图像增强处理算法研究.仪器仪表学报[J].2005,26(8)：44-49.
[5]Naoto Miura, Akio Nagasaka, Takafumi Miyatake, Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification[J].Machine Vision Application,2004,15(2):194-203.
[6]Toshiyuki Tanaka, Naohiko Kubo, Biometric authentication by hand vein patterns[C].SICE, Annual Conference in Sapporo,2004:249-253.
[7]Shinichiro Watanabe, Toshiyuki Tanaka, Eizaburo Iwata. Vein emphasis filter for Biometric Authentication.SICE Annual Conference,2007:690-693.
[8]Fujitsu Laboratories Ltd. Fujitsu Laboratories develops technology for world's first contact-less palm vein pattern biometric authentication system[EB/OL].http://pr.fuiitsu.com/en/new s/2003/03/31.html.
[9]索尼公司(日本东京).索尼开发出手指静脉识别技术"mofiria". [EB/OL].http://ww w.sony51.com/?article-58.html
[10]Ki-chung Chung, Seok Cheol Kee, Sang Ryong Kim,Face recognition using principal component analysis of Gabor filter responses[C] Proc. Int. Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time system.Greece,1999:53-57.
[11]Sang-Kyun Im, Hyung-Man Park, Kim Young-Woo, et al.An Biometric Identification system by Extracting hand Vein Patterns[J].Journal of the Korean Physical Society.2003, 38(3):268-272.
[12]Sang-Kyun Im, Hyung-Man Park, Soo-Won Kim, Chang-Kyung Chung, Hwan-Soo Choi. Improved Vein Pattern Extraction Algorithm and Its Implementation[J].TUMA.2000(1): 2-3.
[13]Kejun Wang, Yan Zhang, Zhi Yuan and Dayan Zhuang. Hand Vein Recognition Based on Multi Supplemental Features of Multi-Classifier Fusion Decision[C].Proceedings of the 2006 IEEE International Conference on Mechatronics and Automation.2006,1970-1974.
[14]林喜荣,庄波,苏晓生等.人体手背血管图像的特征提取及匹配[J].清华大学学报(自然科学版).2003,43(2)：164-167.
[15]周斌,林喜荣,贾惠波,宋榕.多特征融合的手背血管识别算法[J].清华大学学报(自然科学版).2007,47(2)：194-197.
[16]韩笑,马驷良,张忠波.基于脊波变换的手背静脉图像增强及特征提取[J].吉林大学学报(理学版).2006,44(3)：415-417.
[17]李铁钢,马驷良,张忠波,邱菊.基于Bandelet变换的手背静脉识别算法[J].吉林大 学学报(理学版).2007,45(6)：975-979.
[18]吕佩卓,赖声礼.人体手背静脉血管图像增强与特征匹配[J].计算机工程与设计.2007,28(5)：3623-3627.
[19]吕佩卓,赖声礼,陈佳阳,胡蓉.一种自适应的手背静脉区域定位算法[J].为计算机信息.2008,24(2)：208-210.
[20]张晋阳,孙懋珩.手背静脉图像骨架特征提取的算法[J].计算机应用.2007,27(1)：152-154.
[21]任树梅,王高.一种新颖的静脉生物识别技术设计方案[J].测试技术学报.2004,18：220-222.
[22]裘旭光,张大鹏,刘晶,万振凯.基于自适应区域增长算法获取手部静脉模式.计算机工程与应用.2007,43(33)：230-233.
[23]郭跃衡.揭秘手指静脉识别认证系统[J].中国安防,2007,1：60-61.
[24]崔国范,温学兵.人体静脉识别技术概述[J].中国科技信息期刊(健康与生物医药).2007，16：168.
[25]Chih-Lung Lin and Kuo-Chin Fan. Biometric verification using thermal images of palm dorsa vein patterns[J].PAMI.IEEE,2004,14(2),199-214.
[26]David Zhang, W.-K. Kong, J.You, M.Wong. Online Palm-print Identification [J].IEEE Trans. Pattern Analysis and Machine Intelligence,2003,25(9):1041-1050.
[27]Rafael C.Gonzalez, Richard E.Woods.Digital Image Processing(Second Edition).2003: 25-51.
[28]Kenneth R. Castleman著,朱志刚,林学阎,石定机等泽.数字图像处理,电子工业出版社,2002：94-114.
[29]Chih-Lung Lin Kuo-Chin Fan, Biometric verification using thermal images of palm-dorsa vein patterns [J].IEEE Transaction on Circuits System Video Technology.,2004,14(2): 199-213.
[30]Yunxin Wang, Tiegen Liu, Junfeng Jiang. A multi-resolution wavelet algorithm for hand vein pattern recognition.Chinese Optical Letters.2008,6(9):657-670.
[31]Jian-GangWanga,Wei-Yun Yau, Andy Suwandy, Eric Sung. Person recognition by fusing palm-print and palm vein images based on "Laplacian-palm" representation.Pattern Recognition.2008,41:1514-1527.
[32]Lingyu Wang, Graham Leedham, Siu-Yeung Cho.Infrared imaging of hand vein patterns for biometric purposes[J].IET Computer Vision,2007,1(3-4):113-122.
[33]崔建江,宋星月,陈国坤,陈大力.基于几何形状和小波矩的静脉特征提取与匹配[J].东北大学学报(自然科学版),2009,30(9)：1236-1241.
[34]William J. Rucklidge, Efficiently locating objects using Hausdorff distance[J] International Journal of Computer Vision.1997,24:251-270.
[35]Yongsheng Gao, Maylor K.H.Leung. Line segment Hausdorff distance on face matching[J]. Pattern Recognition.2002,35:361-371.
[36]Lingyu Wang, Graham Leedhamb, David Cho. Minutiae feature analysis for infrared hand vein pattern biometrics [J].Pattern Recognition,2008,41 (5):920-929.
[37]John G. Daugman, High confidence visual recognition of persons by a test of statistical Independence[J],IEEE Trans Pattern Analysis and Machine Intelligence,1993,15(11):1148-1161.
[38]Chengjun Liu, Harry Wechsler. Gabor feature based classifycation using the enhanced fisher discriminant model for face recognition[J].IEEE Trans Image Process.,2002,11(4),467-477.
[39]Dennis Gabor, Theory of communication[J],Journal of Inst. Electronic Engineer.,1946, 93(26):429-457,1946.
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