基于步态与人脸融合的远距离身份识别关键技术研究
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摘要
生物特征识别与身份认证是各国高度重视、优先研发的未来安全保障体系关键技术之一。步态识别因其在远距离范围内身份识别的优势,成为该领域最具潜力的新兴热点。但目前该领域的研究仍处于实验室初级阶段。研究开发准确可靠、鲁棒性好、能够实用的步态识别系统仍是十分艰巨的任务。本论文针对远距离行人身份识别要求,根据近几年生物特征识别技术多特征融合的发展趋势,在步态特征提取与身份识别中引入人脸特征信息,借助多信息融合优势对远距离身份识别中的关键技术进行了研究。
研究中采用了中科院自动化研究所建立的CASIA数据库中的数据库DatasetB进行远距离身份识别实验。首先,在视频监控图像中自动提取行人轮廓图像及人脸图像。其次,建立了人体自适应三维数学模型,通过对步态图像序列的跟踪,获取行人的关节角度参数。利用跟踪结果构造模型步态能量图作为步态的整体特征,利用小波变换后的行人下肢关节角度作为步态的动态特征进行步态识别。继而,对人脸图像预处理方法和人脸识别算法进行了实验研究,优选出适合远距离身份识别的人脸识别算法。利用超分辨率图像重建技术提高了人脸图像分辨率从而提高了人脸识别率。最后,利用D-S证据理论、最大法则、最小法则、加法法则、多数投票法等融合算法进行身份识别对比试验,实验结果表明D-S证据理论融合步态和人脸进行身份识别的识别率更高,最终识别率可达94.74%。
本研究的创新之处在于:①建立了人体自适应三维数学模型。该模型可以通过跟踪获取行人关节角度的三维信息,并自动检测行人肢体的宽度信息,可实现对行人的行走姿态及行人外观的个性化表达。②提出了模型步态能量图这一步态特征表达新方法,实验证明模型步态能量图可实现对步态特征的个性化表达。③提出了将跟踪获取的下肢关节角度进行小波变换并用小波变换低频分量联合作为步态动态特征的方法,实验证明经过小波变换后步态识别率提高了3.50%。④提出了基于奇异值扰动的二维主元分析法,并利用结合凸集投影和迭代反投影的超分辨率图像重建技术提高侧面人脸图像的识别率。⑤提出了利用D-S证据理论融合人脸与步态特征的身份识别方法,使远距离身份识别系统的识别率得到有效提高。
Biometric recognition and verification, which has gained highly interest from academia and business, will be one of the key techniques in the security system in the future. Gait recognition has been the most potential research topic for its advantages in the filed of long distance human identification. The research of gait recognition is still in the early lab stage. A reliable and robust gait recognition system which can be used in our daily life still needs extensive efforts. Multimodal biometric fusion is a new study trends in the biometric recognition field. In this thesis, the human identification work was done by fusing gait and face information. The key techniques were studied.
DatasetB of the CASIA gait database provided by the institute of automation, Chinese academy of sciences was used for the human identification experiments. Firstly, human silhouettes and human faces were extracted automatically from the video monitoring images. Secondly, a 3D human body model was created, which was then used to track the gait sequences to obtain the human joints angles. The tracking results were used to create the model based gait energy images (MGEI) used as the holistic gait feature. Wavelet transform was done to the joints angles of the human legs to form the dynamic gait feature. Thirdly, face image preprocessing algorithms and face recognition algorithms were studied and compared by experiments. Super solution image reconstruction techniques were used to increase the resolution of the face images. Finally, fusion algorithms such as D-S evidence theory, max rule, min rule, sum rule and voting rules were used to fuse the gait and face feature. The experiments showed that the recognition rate was the highest after the fusion of gait and face by D-S evidence theory. The recognition rate is 94.74%.
The innovations of the studies were:①A self-adaptive 3D human body model was created. This model could be used to obtain 3D information of the human’s joints angles and human’s shape by human tracking which could represent the human.②A new gait feaure of MGEI was proposed. The experiments showed that MGEI can be used to identify human③. A new gait dynamic feature based on Wavelet transform was proposed. Low frequency component of the Wavelet transform of the human joints angles were used as the gait dynamic feature. Experiments showed that after Wavelet transform the gait recognition rate was increased by 3.50%.④A Singular Value Disturbance based 2DPCA method was proposed for face recognition. And super resolution image reconstruction techniques of POCS and IBP were used to increase the face recognition rate.⑤A new method of fusing gait and face by D-S evidence theory for human identification at a distance was proposed. The human recognition rate was effectively increased.
研究中采用了中科院自动化研究所建立的CASIA数据库中的数据库DatasetB进行远距离身份识别实验。首先,在视频监控图像中自动提取行人轮廓图像及人脸图像。其次,建立了人体自适应三维数学模型,通过对步态图像序列的跟踪,获取行人的关节角度参数。利用跟踪结果构造模型步态能量图作为步态的整体特征,利用小波变换后的行人下肢关节角度作为步态的动态特征进行步态识别。继而,对人脸图像预处理方法和人脸识别算法进行了实验研究,优选出适合远距离身份识别的人脸识别算法。利用超分辨率图像重建技术提高了人脸图像分辨率从而提高了人脸识别率。最后,利用D-S证据理论、最大法则、最小法则、加法法则、多数投票法等融合算法进行身份识别对比试验,实验结果表明D-S证据理论融合步态和人脸进行身份识别的识别率更高,最终识别率可达94.74%。
本研究的创新之处在于:①建立了人体自适应三维数学模型。该模型可以通过跟踪获取行人关节角度的三维信息,并自动检测行人肢体的宽度信息,可实现对行人的行走姿态及行人外观的个性化表达。②提出了模型步态能量图这一步态特征表达新方法,实验证明模型步态能量图可实现对步态特征的个性化表达。③提出了将跟踪获取的下肢关节角度进行小波变换并用小波变换低频分量联合作为步态动态特征的方法,实验证明经过小波变换后步态识别率提高了3.50%。④提出了基于奇异值扰动的二维主元分析法,并利用结合凸集投影和迭代反投影的超分辨率图像重建技术提高侧面人脸图像的识别率。⑤提出了利用D-S证据理论融合人脸与步态特征的身份识别方法,使远距离身份识别系统的识别率得到有效提高。
Biometric recognition and verification, which has gained highly interest from academia and business, will be one of the key techniques in the security system in the future. Gait recognition has been the most potential research topic for its advantages in the filed of long distance human identification. The research of gait recognition is still in the early lab stage. A reliable and robust gait recognition system which can be used in our daily life still needs extensive efforts. Multimodal biometric fusion is a new study trends in the biometric recognition field. In this thesis, the human identification work was done by fusing gait and face information. The key techniques were studied.
DatasetB of the CASIA gait database provided by the institute of automation, Chinese academy of sciences was used for the human identification experiments. Firstly, human silhouettes and human faces were extracted automatically from the video monitoring images. Secondly, a 3D human body model was created, which was then used to track the gait sequences to obtain the human joints angles. The tracking results were used to create the model based gait energy images (MGEI) used as the holistic gait feature. Wavelet transform was done to the joints angles of the human legs to form the dynamic gait feature. Thirdly, face image preprocessing algorithms and face recognition algorithms were studied and compared by experiments. Super solution image reconstruction techniques were used to increase the resolution of the face images. Finally, fusion algorithms such as D-S evidence theory, max rule, min rule, sum rule and voting rules were used to fuse the gait and face feature. The experiments showed that the recognition rate was the highest after the fusion of gait and face by D-S evidence theory. The recognition rate is 94.74%.
The innovations of the studies were:①A self-adaptive 3D human body model was created. This model could be used to obtain 3D information of the human’s joints angles and human’s shape by human tracking which could represent the human.②A new gait feaure of MGEI was proposed. The experiments showed that MGEI can be used to identify human③. A new gait dynamic feature based on Wavelet transform was proposed. Low frequency component of the Wavelet transform of the human joints angles were used as the gait dynamic feature. Experiments showed that after Wavelet transform the gait recognition rate was increased by 3.50%.④A Singular Value Disturbance based 2DPCA method was proposed for face recognition. And super resolution image reconstruction techniques of POCS and IBP were used to increase the face recognition rate.⑤A new method of fusing gait and face by D-S evidence theory for human identification at a distance was proposed. The human recognition rate was effectively increased.
引文
[1]边肇祺,张学工等,模式识别[M],北京,清华大学出版社,2000,1～8
[2]夏鸿斌,须文波,刘渊,生物特征识别技术研究进展[J],计算机工程与应用2003,39(20):77
[3]田捷,杨鑫,生物特征识别技术前景广阔[J],计算机应用,2004,(1)28～31
[4]田捷,杨鑫,生物特征识别技术理论与应用[M],北京:电子工业出版社,2005,6～21
[5] Collins R. Lipton A. kanade T. Introduction to the Special Section on Video Surveillance[J] , IEEE Transaction on Pattern Analysis and Machine Intelligence,2000,22(8):745～746
[6]陈方高升,语音识别技术及发展[J],电信科学,1996(10):54～57
[7]张俊,掌纹识别[D],大连:大连理工大学,2006
[8]田见光,赵荣椿,步态识别综述[J],计算机应用研究,2005,22(5):20～22
[9] Nixon M. Tan T. N. Chellappa R. Human Identification Based on Gait[M],USA:Springer Science and Business Media,2006,6～10
[10] Sourabh A. Niyogi H. Analyzing gait with spatiotemporal surfaces[C],In: Proceedings of IEEE Workshop Non-Rigid Motion,Austin,TX ,USA,1994,24～29
[11] Murase H. Sakai R. Moving object recognition in eigenspace representation: gait analysis and lip reading[J],Pattern Recognition Letters,1996,17(2):155～162
[12] Little J. Boyd J. Recognizing people by their gait: the shape of motion,Journal of Computer Vision Research[J],1998,1(2): 22～32
[13] Shutler J. Nixon M. Harris C. Statistical gait recognition via temporal moments[C], In:Proceedings of IEEE Southwest Symposium on Image Analysis and Interpretation, Austin,Texas,2000,291～295
[14] Hayfron-Acquah J. Nixon M. Carter J. Automatic gait recognition by symmetry analysis[J],Pattern Recognition Letters,2003,24(13):1～9
[15] BenAbdelkader C. Cutler R. Davis L. Motion-based recognition of people in eigengait space[C] , In : Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition,Washington,DC,USA,2002,254～259
[16] Collins R. Cross P. Shi B. Silhouette-based human identification from body shape and gait[C],In:Proceedings of Fifth IEEE International Conference on Automatic Face and Gesture Recognition,Washington DC,USA,2002,351～356
[17] Foster P. Nixon S. Prugel A. Automatic gait recognition using area based metrics[J],Pattern Recognition Letters,2003,24(14):2489～2497
[18] Iwamoto K.Sonobe K. Komatsu N. Gait Recognition Method using HMM[C], In:SICE Annual Conference in Fukui,Fukui,2003A,4～6
[19] Sarkar S. Phillips J. Liu Z. The humanID gait challenge problem: data sets, performance, and analysis[C],IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(2):162～176
[20] Begg R. Palaniswami M. Owen B. Support vector machines for automated gait classification[J],IEEE Transactions on Biomedical Engineering,2005,52(5):828～838
[21] Zhao Y. Liu Y. Li H.et al. 3D gait recognition using multiple cameras[C],In:7th International Conference on Automatic Face and Gesture Recognition, Southampton,2006,529～534
[22] Amin T. Hatzinakos D. A correlation based approach to human gait recognition[C],In: Biometrics Symposium, Baltimore,MD,2007.1～6
[23] Gafurov D. Snekkenes E. Gait recognition using wearable motion recording sensors[J],EURASIP Journal on Advances in Signal Processing,2009,16
[24] Xue Z. J. Ming D. Song W. et al. Infrared gait recognition based on wavelet transform and support vector machine[J],Pattern Recognition,2010,43(8):2904～2910
[25]薛召军,靳静娜,明东等,步态识别研究现状与进展[J],生物医学工程学杂志,2008,25(5):1217～1221
[26] Galton F. Personal identification and description[J],Nature,1889,201～203
[27] Bledsoe W. Man-machine facial recognition[S],Technical Report PRI 22, Panoramic Research Inc, Palo Alto,CA,1966
[28]张翠平,苏光大,人脸识别技术综述[J],中国图像图形学报,2000,A(11)885～894
[29]杨琼,丁晓青,对称主元分析及其在人脸识别中的应用[J],计算机学报,2003,26(9):1146～1151
[30]艾海舟,肖习攀,徐光裙,人脸检测与检索[J],计算机学报,2003,26(7):874～881
[31]杨健,杨静宇,具有统计不相关性的图像投影鉴别分析及人脸识别[J],计算机研究与发展,2003,40(3):447～452
[32]山世光,高文,陈熙霖,基于纹理分布和变形模板的面部特征提取[J],软件学报,2001,12(4):570～577
[33] Li S. Z. Chu R. F. Liao S Z. et al. Illumination invariant face recognition using near-infrared images[J] , IEEE Transactions on Pattern Recogntion and Machine Intelligence,2007,29(4):627～639
[34] Shakhnarovich G. Lee L. Darrell T. Integrated face and gait recognition from multiple views[C],In:2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition,Kauai,Hawaii,2001,439
[35] Geng X. Wang L. Li M. et al. Adaptive fusion of gait and race for human identification in video[C],In:Applications of Computer Vision,Copper Mountain,CO,2008,1～6
[36] Huang S. Harris J. Nixon S. Canonical space representation for recognizing humans by gait and face[C],In:Proceedings of IEEE Southwest Symposium on Image Analysis and Interpretation,Tucson,AZ,USA,180～185
[37] Zhou X. L. Bhanu B. Feature fusion of face and gait for human recognition at a distance in video[C] , In : 18th International Conference on Pattern Recognition,Hong Kong,2006,4,529～532
[38] Zhou X. L. Bhanu B. Feature fusion of side face and gait for vide-based human identification[J],Pattern Recognition,2008,41,778～795
[39] Hou X. H. Liu Z. J. Fusion of face and gait for human recognition in video sequences[C],In:2009 International Conference on Information Technology and Computer Science,Kiev, Ukraine,2009,1,577～580
[40] Kale A. RoyChowdhurry K. Chellappa R. Fusion of gait and face for human identification[C],In:IEEE International Conference on Acoustics,Speech,and Signal Processing,Montreal,Que,Canada,2004,5,V-901
[41] Shakhnarovich G. Darrell T. On probabilistic combination of face and gait cues for identification[C],In:Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition, Washington, DC,USA,2002,169～174
[42] Chellappa R. RoyChowdhurry K. Kale A. Human identification using gait and face[C],In:IEEE Conference on Computer Vision and Pattern Recognition,Minneapolis,MN,2007,1～2
[43] Zhou X. L. Bhanu B. Integrating face and gait for human recognition[C], In: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop,2006,55~58
[44] Olson T. Moving object detection and event recognition algorithm for smart cameras[C],In:Proceedings of the DARPA Image Understanding Workshop, 1997,159～176
[45] Haritaoglu I. Harwood D. Davis S. W4 : real-time surveillance of people and their activities[J] , IEEE Transaction on Pattern Analysis and Machine Intelligence,2000,22(8):809～822
[46] Stauffer C. Grimson W. Adaptive background mixture models for real time tracking[C], In:Proceeding of the IEEE CS Conference on Computer Vision and Pattern Recognition,Collins,IEEE Computer Society,1999,246～252
[47] McKenna S. Jabri S. Duric Z. et al. Tracking groups of people[J],Computer Vision and Image Understanding,2000,80(1):42～56
[48] Xue Z. J. Wang D. H. Ming D. et al. New gait recognition technique used in functional electrical stimulation system control[C],In:Proceedings of the World Congress on Intelligent Control and Automation,2006,9421～9424
[49] Little J. Boyd J. Recognizing people by their gait:the shape of motion[J],Videre,1998,1(2):1～33
[50] Bobick A. Johnson A. Gait recognition using static activity-specific parameters[C] , In : Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2001,1,I423～I430
[51] BenAbdelkader C. Cutler R. Davis L. Motion-based recognition of people in eigengait space[C],In:Proceedings of the International Conference on Automatic Face and Gesture Recognition, Washington, DC,USA ,2002,267～272
[52] Lee L. Grimson W. Gait analysis for recognition and classification[C],In: Proceeding of International Conference on Automatic Face and Gesture Recognition , Washington,DC,USA,155～162
[53] Kale A. Rajagopalan A. Cuntoor N. et al. Gait-based recognition of humans using continuous HMMs[C],In: Proceeding of International Conference on Automatic Face and Gesture Recognition,Washington,DC,USA,336～341
[54] Vega R. Sarkar S. Representation of the evolution of feature relationship statistics: human gait-based recognition[J],IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(10):1323～1328
[55] CASIA Gait Database[DB],http://www.sinobiometrics.com
[56] Yang G. Huang T-S. Human face detection in complex background[J],Pattern Recognition,1994,27(1):53～63
[57] Sung K-K. Poggio T. Example-based learning for view based human facedetection[J] , IEEE Transactions on Pattern Analysisi and Machine Inteligence,1998,20(1):39～51
[58] Pentland A. Moghaddam B. Starner T. View-based and modular eigenspaces for face recognition[C],In:Proceeding of the IEEE Conference On Computer Vision and Pattern Recognition,Seattle,WA,1994,21～23
[59] Moghaddam B. Pentland A. Face recognition using view-based and modular eigenspaces[C],In:IEEE Computer Society Conference on Computer Vision and Pattern Recognition,Seattle,WA,USA,1994,12～21
[60] Turk M. Pentland A. Eigenfaces for recognition[J],Journal of Cognitive Neuroscience,1991,3(1):71～86
[61]牛朝炜,王增福,基于彩色和运动信息的人脸检测[J],模式识别与人工智能,2002,15(2):205～210
[62] Jones J. Rehg M. Statistical color models with application to skin detection[C],In:IEEE Computer Society Conference on Computer Vision and Pattern Recognition,Fort Collins,CO,USA,1999,274～280
[63] Tie Y. Guan L. Automatic face detection in video sequences using local normalization and optimal adaptive correlation technique[J] , Pattern Recognition,2009,42(9):1859～1868
[64]刘艳丽,赵跃龙,人脸识别技术研究进展[J],计算机工程,2005,31(3):10～12
[65] Yamauchi K. Bhanu B. Saito H. Recogntion of walking humans in 3D: initial results[C],In:IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops,Miami,FL,2009,45～52
[66] Barron C. Kakadiaris I. A convex penaltymethod for optical human motion tracking[C],In:Proceeding of First ACM MIGMM International Workshop on Video Surveillance,Berkeley,2003,1～10
[67] Baumberg A. Hogg D. An efficientmethod for contour tracking using active shapemodels[C],In:Proceeding of IEEE Workshop on Motion of Non-Rigid and Articulated Objects,Austin, TX,USA,1994,194～199
[68] Murray P. Drought B. Kory R C. Walking patterns of normal men [J]. Bone and Joint Surgery,1964,46-A(2):335～360
[69]王亮,胡卫明,谭铁牛,基于步态的身份识别[J],计算机学报,2003,26(3):1～7
[70] Collins T. Gross R. Jianbo S. Silhouette-based human identification from body shape and gait[C],In:Proceedings of Fifth IEEE International Conference on Automatic Face and Gesture Recognition,Washington DC,USA,2002,351～356
[71]吴清江,许文芳,王青力,基于人体轮廓中线投影的步态特征提取[J],计算机工程,2006,32(24):192～194
[72] Liu Z. Sarkar S. Improved gait recognition by gait dynamics normalization[J] , IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(6):863～876
[73]陈实,马天骏,高有行,用行人轮廓的分布直方图分类和识别步态[J],计算机研究与进展,2009,46(2):295~301
[74] Kapur N. Sahoo K. Wong C. A new method for grey-level picture thresholding using the entropy of the histogram[J],Computer Vision Graphics and Image Processing,1995,29,273～285
[75]薛景浩,章毓晋,林行刚,图像分割中的交叉熵和模糊散度算法[J],电子学报,1999,22 (10):131～134
[76]刘文萍,吴立德,一种对空中目标图像自适应分割方法[J],红外与毫米波学报,1996,15(4):257～261
[77] Lee C. Elgammal A. Gait tracking and recognition using person-dependent dynamic shape model[C],In:7th International Conference on Automatic Face and Gesture Recognition,Southampton,2006,553～559
[78] Chen S. Gao Y. An invariant appearance model for gait recognition[C],In:IEEE International Conference on Multimedia and Expo,BeiJing,2007,1375～1378
[79] Enokida S. Shimomoto R. Wada T. et al. A predictive model for gait recognition[C],In:2006 Biometrics Symposium:Special Session on Research at the Biometric Consortium Conference,Baltimore,MD,2006,1～6
[80] Huang X. Boulgouris V. Model-based human gait recognition using fusion of features[C],In:IEEE International Conference on Acoustics, Speech and Signal Processing,TaiBei,2009,1469～1472
[81] Ming D. Zhang C. Bai Y. et al. Gait recognition based on multiple views fusion of wavelet descriptor and human skeleton model[C],In:IEEE International Conference on Virtual Environments,HongKong,2009,246～249
[82] Kim D. Paik J. Gait recognition using active shape model and motion prediction[J],Computer Vision,2010,4(1):25～36
[83] Xu J. Cong W. Li J. et al. Gait recognition based on key frame and elliptical model[C],In:IEEE International Conference on Information and Automation,Harbin,2010,2483～2487
[84] Han J. Bhanu B. Individual recognition using gait energy image.,IEEE Transactions on Pattern Analysis and Machine Intelligence[J],2006,28(2):316～322
[85] Harries L. Diffraction and resolving power[J],Journal of the Optical Societyof America,1999,4(2),105～109
[86] Goodman W. Introduction to Fourier optics[M],Greewood Village: Scion Publishing Ltd,2005,31～60
[87] Lukosz W. Optical systems with resolving power exceeding the classical limit[J],Journal of the Optical Society of America,1966,56(11),1463～1472
[88] Huang T. S. Advances in computer vision and image processing[M],Greenwich, CT:JAI,1984:317～339
[89]袁小华,欧阳晓丽,夏德深,超分辨率图像恢复研究综述[J],地理与地理信息科学,2006,22(3),43～47
[90] Elad M. Feuer A. Restoration of a single super-resolution image from several blurred, noisy and under sampled measured images[J],IEEE Transactions on Image Processing,1977,6(12):1646～1658
[91]肖创柏,禹晶,薛毅,一种基于MAP的超分辨率图像重建的快速算法[J],计算机研究与进展,2009,46(5):872～880
[92] Irani M. Peleg S. Improving resolution by image registration[J],Graph Models Image Process,1991,53(3):231～329
[93]邓乾国,游志胜,基于凸集投影算法的超分辨率图像重建技术[J],成都信息工程学院学报,2005,20(6):708～711
[94]袁小华,欧阳晓丽,夏德深,超分辨率图像恢复研究综述[J],地理与地理信息科学,2006,22(3):43～47
[95] Brunelli R. Falavigna D.Person identification using multiples cues[J],IEEE Transactions on Pattern Analysis and Machine Intelligence,1995,12(10):955～966
[96] Bigun S. Bigun J. Maitre G. et al.Fusion of audio and video information for multi-modal person authentication[J],Pattern Recognition Letters,1997,18(9):835～843
[97] Hong L , Jain A K . Integration faces and fingerprints for personal identification[J] . IEEE Transaction on Pattern Analysis and Machine Intelligence,1998,20(12):1295～1300
[98] Hong L. Jain K.Pankanti S.Can multi-biometrics improve performance[C]? In:Proceedings of IEEE Workshop on Automatic Identification Advanced Technologies,1999.59～64
[99] Ross A. Jain A. Qian Z.Information fusion in biometrics[J],Pattern Recognition Letters,2003,24(13):2115～2125
[100] Verlinde P. A contribution to muti-modal identity verification using decisionfusion[D],PhD Thesis,Department of Signal and Image Processing,Telecom Paris,France,1999.
[101] Ribaric S. Fratrie I.A biometric identification system based on eigenpalm and eigenfinger features[J],IEEE Transaction on Pattern Recogntion and Machine Intelligence,2005,27(11):1698～1709
[102] Park C. Choi T. Gim Y. et al.Multi-modal human verification using face and speech[C],In:Proceedings of the Fourth IEEE International Conference on Computer Vision Systems,2006,54～59
[103] Burge M. Buyer W. Using ear biometrics for passive identification[C],In:14th Internatinnal Information Security Conference,VieBna,Austria,Kluwer Academic,1998,139～148
[104] Frischholz R. Dleckemann U. BioID: a multimodal biometric identification system[J],Computer,2000,33(2):64～68
[105] Dempster A. Upper and lower probabilities induced by multivalued mapping[J], Annals of Mathematical Statistics,1967,38(2):325～339
[2]夏鸿斌,须文波,刘渊,生物特征识别技术研究进展[J],计算机工程与应用2003,39(20):77
[3]田捷,杨鑫,生物特征识别技术前景广阔[J],计算机应用,2004,(1)28～31
[4]田捷,杨鑫,生物特征识别技术理论与应用[M],北京:电子工业出版社,2005,6～21
[5] Collins R. Lipton A. kanade T. Introduction to the Special Section on Video Surveillance[J] , IEEE Transaction on Pattern Analysis and Machine Intelligence,2000,22(8):745～746
[6]陈方高升,语音识别技术及发展[J],电信科学,1996(10):54～57
[7]张俊,掌纹识别[D],大连:大连理工大学,2006
[8]田见光,赵荣椿,步态识别综述[J],计算机应用研究,2005,22(5):20～22
[9] Nixon M. Tan T. N. Chellappa R. Human Identification Based on Gait[M],USA:Springer Science and Business Media,2006,6～10
[10] Sourabh A. Niyogi H. Analyzing gait with spatiotemporal surfaces[C],In: Proceedings of IEEE Workshop Non-Rigid Motion,Austin,TX ,USA,1994,24～29
[11] Murase H. Sakai R. Moving object recognition in eigenspace representation: gait analysis and lip reading[J],Pattern Recognition Letters,1996,17(2):155～162
[12] Little J. Boyd J. Recognizing people by their gait: the shape of motion,Journal of Computer Vision Research[J],1998,1(2): 22～32
[13] Shutler J. Nixon M. Harris C. Statistical gait recognition via temporal moments[C], In:Proceedings of IEEE Southwest Symposium on Image Analysis and Interpretation, Austin,Texas,2000,291～295
[14] Hayfron-Acquah J. Nixon M. Carter J. Automatic gait recognition by symmetry analysis[J],Pattern Recognition Letters,2003,24(13):1～9
[15] BenAbdelkader C. Cutler R. Davis L. Motion-based recognition of people in eigengait space[C] , In : Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition,Washington,DC,USA,2002,254～259
[16] Collins R. Cross P. Shi B. Silhouette-based human identification from body shape and gait[C],In:Proceedings of Fifth IEEE International Conference on Automatic Face and Gesture Recognition,Washington DC,USA,2002,351～356
[17] Foster P. Nixon S. Prugel A. Automatic gait recognition using area based metrics[J],Pattern Recognition Letters,2003,24(14):2489～2497
[18] Iwamoto K.Sonobe K. Komatsu N. Gait Recognition Method using HMM[C], In:SICE Annual Conference in Fukui,Fukui,2003A,4～6
[19] Sarkar S. Phillips J. Liu Z. The humanID gait challenge problem: data sets, performance, and analysis[C],IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(2):162～176
[20] Begg R. Palaniswami M. Owen B. Support vector machines for automated gait classification[J],IEEE Transactions on Biomedical Engineering,2005,52(5):828～838
[21] Zhao Y. Liu Y. Li H.et al. 3D gait recognition using multiple cameras[C],In:7th International Conference on Automatic Face and Gesture Recognition, Southampton,2006,529～534
[22] Amin T. Hatzinakos D. A correlation based approach to human gait recognition[C],In: Biometrics Symposium, Baltimore,MD,2007.1～6
[23] Gafurov D. Snekkenes E. Gait recognition using wearable motion recording sensors[J],EURASIP Journal on Advances in Signal Processing,2009,16
[24] Xue Z. J. Ming D. Song W. et al. Infrared gait recognition based on wavelet transform and support vector machine[J],Pattern Recognition,2010,43(8):2904～2910
[25]薛召军,靳静娜,明东等,步态识别研究现状与进展[J],生物医学工程学杂志,2008,25(5):1217～1221
[26] Galton F. Personal identification and description[J],Nature,1889,201～203
[27] Bledsoe W. Man-machine facial recognition[S],Technical Report PRI 22, Panoramic Research Inc, Palo Alto,CA,1966
[28]张翠平,苏光大,人脸识别技术综述[J],中国图像图形学报,2000,A(11)885～894
[29]杨琼,丁晓青,对称主元分析及其在人脸识别中的应用[J],计算机学报,2003,26(9):1146～1151
[30]艾海舟,肖习攀,徐光裙,人脸检测与检索[J],计算机学报,2003,26(7):874～881
[31]杨健,杨静宇,具有统计不相关性的图像投影鉴别分析及人脸识别[J],计算机研究与发展,2003,40(3):447～452
[32]山世光,高文,陈熙霖,基于纹理分布和变形模板的面部特征提取[J],软件学报,2001,12(4):570～577
[33] Li S. Z. Chu R. F. Liao S Z. et al. Illumination invariant face recognition using near-infrared images[J] , IEEE Transactions on Pattern Recogntion and Machine Intelligence,2007,29(4):627～639
[34] Shakhnarovich G. Lee L. Darrell T. Integrated face and gait recognition from multiple views[C],In:2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition,Kauai,Hawaii,2001,439
[35] Geng X. Wang L. Li M. et al. Adaptive fusion of gait and race for human identification in video[C],In:Applications of Computer Vision,Copper Mountain,CO,2008,1～6
[36] Huang S. Harris J. Nixon S. Canonical space representation for recognizing humans by gait and face[C],In:Proceedings of IEEE Southwest Symposium on Image Analysis and Interpretation,Tucson,AZ,USA,180～185
[37] Zhou X. L. Bhanu B. Feature fusion of face and gait for human recognition at a distance in video[C] , In : 18th International Conference on Pattern Recognition,Hong Kong,2006,4,529～532
[38] Zhou X. L. Bhanu B. Feature fusion of side face and gait for vide-based human identification[J],Pattern Recognition,2008,41,778～795
[39] Hou X. H. Liu Z. J. Fusion of face and gait for human recognition in video sequences[C],In:2009 International Conference on Information Technology and Computer Science,Kiev, Ukraine,2009,1,577～580
[40] Kale A. RoyChowdhurry K. Chellappa R. Fusion of gait and face for human identification[C],In:IEEE International Conference on Acoustics,Speech,and Signal Processing,Montreal,Que,Canada,2004,5,V-901
[41] Shakhnarovich G. Darrell T. On probabilistic combination of face and gait cues for identification[C],In:Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition, Washington, DC,USA,2002,169～174
[42] Chellappa R. RoyChowdhurry K. Kale A. Human identification using gait and face[C],In:IEEE Conference on Computer Vision and Pattern Recognition,Minneapolis,MN,2007,1～2
[43] Zhou X. L. Bhanu B. Integrating face and gait for human recognition[C], In: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop,2006,55~58
[44] Olson T. Moving object detection and event recognition algorithm for smart cameras[C],In:Proceedings of the DARPA Image Understanding Workshop, 1997,159～176
[45] Haritaoglu I. Harwood D. Davis S. W4 : real-time surveillance of people and their activities[J] , IEEE Transaction on Pattern Analysis and Machine Intelligence,2000,22(8):809～822
[46] Stauffer C. Grimson W. Adaptive background mixture models for real time tracking[C], In:Proceeding of the IEEE CS Conference on Computer Vision and Pattern Recognition,Collins,IEEE Computer Society,1999,246～252
[47] McKenna S. Jabri S. Duric Z. et al. Tracking groups of people[J],Computer Vision and Image Understanding,2000,80(1):42～56
[48] Xue Z. J. Wang D. H. Ming D. et al. New gait recognition technique used in functional electrical stimulation system control[C],In:Proceedings of the World Congress on Intelligent Control and Automation,2006,9421～9424
[49] Little J. Boyd J. Recognizing people by their gait:the shape of motion[J],Videre,1998,1(2):1～33
[50] Bobick A. Johnson A. Gait recognition using static activity-specific parameters[C] , In : Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2001,1,I423～I430
[51] BenAbdelkader C. Cutler R. Davis L. Motion-based recognition of people in eigengait space[C],In:Proceedings of the International Conference on Automatic Face and Gesture Recognition, Washington, DC,USA ,2002,267～272
[52] Lee L. Grimson W. Gait analysis for recognition and classification[C],In: Proceeding of International Conference on Automatic Face and Gesture Recognition , Washington,DC,USA,155～162
[53] Kale A. Rajagopalan A. Cuntoor N. et al. Gait-based recognition of humans using continuous HMMs[C],In: Proceeding of International Conference on Automatic Face and Gesture Recognition,Washington,DC,USA,336～341
[54] Vega R. Sarkar S. Representation of the evolution of feature relationship statistics: human gait-based recognition[J],IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(10):1323～1328
[55] CASIA Gait Database[DB],http://www.sinobiometrics.com
[56] Yang G. Huang T-S. Human face detection in complex background[J],Pattern Recognition,1994,27(1):53～63
[57] Sung K-K. Poggio T. Example-based learning for view based human facedetection[J] , IEEE Transactions on Pattern Analysisi and Machine Inteligence,1998,20(1):39～51
[58] Pentland A. Moghaddam B. Starner T. View-based and modular eigenspaces for face recognition[C],In:Proceeding of the IEEE Conference On Computer Vision and Pattern Recognition,Seattle,WA,1994,21～23
[59] Moghaddam B. Pentland A. Face recognition using view-based and modular eigenspaces[C],In:IEEE Computer Society Conference on Computer Vision and Pattern Recognition,Seattle,WA,USA,1994,12～21
[60] Turk M. Pentland A. Eigenfaces for recognition[J],Journal of Cognitive Neuroscience,1991,3(1):71～86
[61]牛朝炜,王增福,基于彩色和运动信息的人脸检测[J],模式识别与人工智能,2002,15(2):205～210
[62] Jones J. Rehg M. Statistical color models with application to skin detection[C],In:IEEE Computer Society Conference on Computer Vision and Pattern Recognition,Fort Collins,CO,USA,1999,274～280
[63] Tie Y. Guan L. Automatic face detection in video sequences using local normalization and optimal adaptive correlation technique[J] , Pattern Recognition,2009,42(9):1859～1868
[64]刘艳丽,赵跃龙,人脸识别技术研究进展[J],计算机工程,2005,31(3):10～12
[65] Yamauchi K. Bhanu B. Saito H. Recogntion of walking humans in 3D: initial results[C],In:IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops,Miami,FL,2009,45～52
[66] Barron C. Kakadiaris I. A convex penaltymethod for optical human motion tracking[C],In:Proceeding of First ACM MIGMM International Workshop on Video Surveillance,Berkeley,2003,1～10
[67] Baumberg A. Hogg D. An efficientmethod for contour tracking using active shapemodels[C],In:Proceeding of IEEE Workshop on Motion of Non-Rigid and Articulated Objects,Austin, TX,USA,1994,194～199
[68] Murray P. Drought B. Kory R C. Walking patterns of normal men [J]. Bone and Joint Surgery,1964,46-A(2):335～360
[69]王亮,胡卫明,谭铁牛,基于步态的身份识别[J],计算机学报,2003,26(3):1～7
[70] Collins T. Gross R. Jianbo S. Silhouette-based human identification from body shape and gait[C],In:Proceedings of Fifth IEEE International Conference on Automatic Face and Gesture Recognition,Washington DC,USA,2002,351～356
[71]吴清江,许文芳,王青力,基于人体轮廓中线投影的步态特征提取[J],计算机工程,2006,32(24):192～194
[72] Liu Z. Sarkar S. Improved gait recognition by gait dynamics normalization[J] , IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(6):863～876
[73]陈实,马天骏,高有行,用行人轮廓的分布直方图分类和识别步态[J],计算机研究与进展,2009,46(2):295~301
[74] Kapur N. Sahoo K. Wong C. A new method for grey-level picture thresholding using the entropy of the histogram[J],Computer Vision Graphics and Image Processing,1995,29,273～285
[75]薛景浩,章毓晋,林行刚,图像分割中的交叉熵和模糊散度算法[J],电子学报,1999,22 (10):131～134
[76]刘文萍,吴立德,一种对空中目标图像自适应分割方法[J],红外与毫米波学报,1996,15(4):257～261
[77] Lee C. Elgammal A. Gait tracking and recognition using person-dependent dynamic shape model[C],In:7th International Conference on Automatic Face and Gesture Recognition,Southampton,2006,553～559
[78] Chen S. Gao Y. An invariant appearance model for gait recognition[C],In:IEEE International Conference on Multimedia and Expo,BeiJing,2007,1375～1378
[79] Enokida S. Shimomoto R. Wada T. et al. A predictive model for gait recognition[C],In:2006 Biometrics Symposium:Special Session on Research at the Biometric Consortium Conference,Baltimore,MD,2006,1～6
[80] Huang X. Boulgouris V. Model-based human gait recognition using fusion of features[C],In:IEEE International Conference on Acoustics, Speech and Signal Processing,TaiBei,2009,1469～1472
[81] Ming D. Zhang C. Bai Y. et al. Gait recognition based on multiple views fusion of wavelet descriptor and human skeleton model[C],In:IEEE International Conference on Virtual Environments,HongKong,2009,246～249
[82] Kim D. Paik J. Gait recognition using active shape model and motion prediction[J],Computer Vision,2010,4(1):25～36
[83] Xu J. Cong W. Li J. et al. Gait recognition based on key frame and elliptical model[C],In:IEEE International Conference on Information and Automation,Harbin,2010,2483～2487
[84] Han J. Bhanu B. Individual recognition using gait energy image.,IEEE Transactions on Pattern Analysis and Machine Intelligence[J],2006,28(2):316～322
[85] Harries L. Diffraction and resolving power[J],Journal of the Optical Societyof America,1999,4(2),105～109
[86] Goodman W. Introduction to Fourier optics[M],Greewood Village: Scion Publishing Ltd,2005,31～60
[87] Lukosz W. Optical systems with resolving power exceeding the classical limit[J],Journal of the Optical Society of America,1966,56(11),1463～1472
[88] Huang T. S. Advances in computer vision and image processing[M],Greenwich, CT:JAI,1984:317～339
[89]袁小华,欧阳晓丽,夏德深,超分辨率图像恢复研究综述[J],地理与地理信息科学,2006,22(3),43～47
[90] Elad M. Feuer A. Restoration of a single super-resolution image from several blurred, noisy and under sampled measured images[J],IEEE Transactions on Image Processing,1977,6(12):1646～1658
[91]肖创柏,禹晶,薛毅,一种基于MAP的超分辨率图像重建的快速算法[J],计算机研究与进展,2009,46(5):872～880
[92] Irani M. Peleg S. Improving resolution by image registration[J],Graph Models Image Process,1991,53(3):231～329
[93]邓乾国,游志胜,基于凸集投影算法的超分辨率图像重建技术[J],成都信息工程学院学报,2005,20(6):708～711
[94]袁小华,欧阳晓丽,夏德深,超分辨率图像恢复研究综述[J],地理与地理信息科学,2006,22(3):43～47
[95] Brunelli R. Falavigna D.Person identification using multiples cues[J],IEEE Transactions on Pattern Analysis and Machine Intelligence,1995,12(10):955～966
[96] Bigun S. Bigun J. Maitre G. et al.Fusion of audio and video information for multi-modal person authentication[J],Pattern Recognition Letters,1997,18(9):835～843
[97] Hong L , Jain A K . Integration faces and fingerprints for personal identification[J] . IEEE Transaction on Pattern Analysis and Machine Intelligence,1998,20(12):1295～1300
[98] Hong L. Jain K.Pankanti S.Can multi-biometrics improve performance[C]? In:Proceedings of IEEE Workshop on Automatic Identification Advanced Technologies,1999.59～64
[99] Ross A. Jain A. Qian Z.Information fusion in biometrics[J],Pattern Recognition Letters,2003,24(13):2115～2125
[100] Verlinde P. A contribution to muti-modal identity verification using decisionfusion[D],PhD Thesis,Department of Signal and Image Processing,Telecom Paris,France,1999.
[101] Ribaric S. Fratrie I.A biometric identification system based on eigenpalm and eigenfinger features[J],IEEE Transaction on Pattern Recogntion and Machine Intelligence,2005,27(11):1698～1709
[102] Park C. Choi T. Gim Y. et al.Multi-modal human verification using face and speech[C],In:Proceedings of the Fourth IEEE International Conference on Computer Vision Systems,2006,54～59
[103] Burge M. Buyer W. Using ear biometrics for passive identification[C],In:14th Internatinnal Information Security Conference,VieBna,Austria,Kluwer Academic,1998,139～148
[104] Frischholz R. Dleckemann U. BioID: a multimodal biometric identification system[J],Computer,2000,33(2):64～68
[105] Dempster A. Upper and lower probabilities induced by multivalued mapping[J], Annals of Mathematical Statistics,1967,38(2):325～339
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