期刊文献+

基于人脸和人耳的多模态生物特征识别 被引量:4

Multimodal recognition using face and ear
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摘要 单一模式生物特征识别系统由于存在一些固有的局限性,有时难以满足实际应用的需求,本文提出了基于正面人脸和人耳信息融合的多模态生物特征识别方法.针对USTB人耳图像库和ORL人脸图像库,利用核Fisher鉴别分析方法分别进行了人耳识别、人脸识别和人脸人耳融合识别,融合策略包括图像层融合和特征层融合两种.识别结果表明基于人脸人耳信息融合的多模态识别的识别率优于单体的人耳或人脸识别.这说明融合多种生物特征的多模态识别可以提高身份认证的准确率,也为实现非打扰式识别提供了一种新的途径. Unimodal biometric systems have to contend with a variety of problems and sometimes cannot satisfy application requirements.In this paper,a novel method of multimodal recognition using frontal face and ear was proposed.Kernel Fisher Discriminant Analysis was used for ear recognition,face recognition and the multimodal recognition.The multimodal recognition was studied on the image level fusion and feature level fusion.The experimental results from using USTB ear database and ORL face database show that the multimodal recognition outperforms the unimodal biometric recognition.This work shows that multibiometric system can increase the accuracy of overall system recognition,and provides an effective approach of non-intrusive biometric recognition.
出处 《北京科技大学学报》 EI CAS CSCD 北大核心 2007年第S2期190-193,共4页 Journal of University of Science and Technology Beijing
基金 国家自然科学基金资助项目(No60573058) 北京市教育委员会重点学科共建项目资助(NoXK100080537)
关键词 人耳识别 人脸识别 多模态生物特征识别 核Fisher鉴别分析算法 ear recognition face recognition multimodal recognition Kernel Fisher Discriminant Analysis
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参考文献12

  • 1陈才扣,杨静宇,杨健.基于组合子空间的最优特征抽取及人脸识别[J].信号处理,2004,20(6):609-612. 被引量:4
  • 2刘青山,卢汉清,马颂德.综述人脸识别中的子空间方法[J].自动化学报,2003,29(6):900-911. 被引量:117
  • 3Wang Y,Tan T,Jain A.Combining face and iris biometrics for identity verification. Proc.of4th International Conference on Audio Video Based Pattern Analysis . 2003 被引量:1
  • 4Yuan L,Mu Z C,Zhang Y,et al.Ear recognition using i m-proved non-negative matrix factorization. 18th International Conference on Pattern Recognition . 2006 被引量:1
  • 5Yang J,,Yang J Y.Opti mal FLDalgorithmfor facial feature ex-traction. SPIE Processing of the Intelligent Robots and Com-puter Vision XX:Algorithms,Techniques,and Active Vision . 2001 被引量:1
  • 6Phillips P J,Grother P,Micheals R J,et al.Face RecognitionVendor Test2002:Evaluation Report. Technical Report,NIS-TIR6965 . 2003 被引量:1
  • 7Ross A,Jain A.Multi modal biometrics:an overview. Proc.of12th European Processing Conference . 2004 被引量:1
  • 8Yacoub S B,Abdeljaoued Y.Fusion of face and speech data for person identity verification. IEEE Tran Neur Networks . 1999 被引量:1
  • 9Zhou X L,Bhanu B.Integratingface and gait for humanrecogni-tion. Proc.of Conference on Computer Vision and Pattern Recognition . 2006 被引量:1
  • 10Chang K,,Bowyer K,Flynn P.Multi-biometrics using facial ap-pearance,shape and temperature. Proc.of the Sixth IEEE In-ternational Conference on Automatic Face and Gesture Recogni-tion . 2004 被引量:1

二级参考文献79

  • 1Hjelmas E, Low B K. Face detection: A survey. Journal of Computer Vision and Image Understanding, 2001, 83(3) : 236-274. 被引量:1
  • 2Yang M H, Ahuja N, Kriegman D. Detecting faces in images: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(1): 34-58. 被引量:1
  • 3Toyama K. Prolegomena for robust face tracking. MSR- Tech-Report-98-65, Microsoft, 1998. 被引量:1
  • 4Samal A, lyengar P. Automatic recognition and analysis of human faces and facial expressions: A survey. Pattern recognition, 1992, 25(1) : 65--77. 被引量:1
  • 5Zhao W, Chellappa R, Rosenfeld A, Phillips P J. Face recognition- A literature survey. CS-Tech Report-4167, University of Maryland, 2000. 被引量:1
  • 6Zhou J, Lu C Y, Zhang C S, Li Y D. A survey of face recognition. Acta Electronica Sinica, 2000, 28(4) : 102--106(in Chinese). 被引量:1
  • 7Chellappa R, Wilson C L, Sirohey S. Human and machine recognition of faces: A survey. Proceedings of the IEEE,1995, 83(5): 705--740. 被引量:1
  • 8Bledsoe W. Man-machine facial recognition. Tech Report PRI-22, Panoramic Research Inc., Palo Alto, CA, 1966. 被引量:1
  • 9Belhumeur P N, Hespanha J P, Kriegman D J. Eigenfaces vs Fisherfaee: Recognition using class special linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7) : 711-720. 被引量:1
  • 10Zhao W, Chellappa R, Krishnaswamy A. Discriminant analysis of principal components for face recognition. In:Proceedings of International Conference on Automatic Face and Gesture Recognition, Japan: Nara, 1998. 336-341. 被引量:1

共引文献119

同被引文献28

  • 1张海军,穆志纯,危克.人耳识别技术研究进展综述[J].计算机工程与应用,2004,40(33):5-7. 被引量:17
  • 2袁立,穆志纯,徐正光,刘克.基于人耳生物特征的身份识别[J].模式识别与人工智能,2005,18(3):310-315. 被引量:25
  • 3张海军,穆志纯,张克君,张成阳.基于主元分析的人耳图像识别方法[J].北京工商大学学报(自然科学版),2005,23(6):28-30. 被引量:4
  • 4Chen H, Bhanu B. Efficient recognition of highly similar 3D objects in range images. IEEE Trans Pattern Anal Mach lntell, 2009, 31 ( 1 ) : 172. 被引量:1
  • 5Mu Z C, Yuan L, Xu Z G. Shape and structural feature based ear recognition//Proceedings of Advances in Biometric Person Authentication. Guangzhou, 2004:663. 被引量:1
  • 6Hurley D, Nixon M, Carter J. Force field feature extraction for ear biometrics. Comput Vision Image Understanding, 2005, 98 : 491. 被引量:1
  • 7Chang K, Bowyer K, Sarkar S, et al. Comparison and combination of ear and face images in appearance-based biometrics. 1EEE Trans Pattern Anal Mach lnteU , 2003, 25(9) : 1160. 被引量:1
  • 8Xie Z X, Mu Z C. Ear recognition using LLE and IDLLE algorithm//Proceedings of 19th International Conference on Pattern Recognition. Florida, 2008 : 1. 被引量:1
  • 9Dun W J, Mu Z C. Multi-modal recognition of face and ear images based on two types of independent component analysis. J Comput lnfSyst, 2008, 4(5) : 1977. 被引量:1
  • 10Liu C J. Gabor-based kernel PCA with fractional power polynomial models for face recognition. IEEE Trans Pattern Anal Mach lntell, 2004, 26(5) : 572. 被引量:1

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