期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

基于特征融合和流形增强的视频人脸识别 被引量：5

Video Face Recognition Based on Feature Fusion and Manifold Enhancement
在线阅读 下载PDF
导出
摘要 提出一种在视频环境下的人脸识别算法(FFME)。将人脸划分为不同的区域,并融合针对人脸不同区域变化特点的不同类型局部特征,建立K-NN模型,根据sum rule划分人脸分类,利用流形建立参考人脸图集,以此重排分类结果,增强人脸识别准确率。在视频人脸数据库Mobo数据集和Honda/UCSD数据集上的实验结果表明,FFME的识别性能优于主成分分析、线性鉴别分析、隐马尔科夫模型、局部线性嵌入,以及流形距离等方法。 A new approach of face recognition in videos, named FFME, is proposed. Faces are divided into different regions. Different local feature descriptors, which are selected against the variation of some region, are combined for face representation. K-NN model is built for feature vectors classification. A sum rule is followed by FFME to combine the individual classification results. Manifold is introduced for constructing a set of reference face images to re-rank the top candidate images, which is approved later to enhance the face recognition rates. Experimental results on the video-based face database Mobo and Itonda/UCSD show that FFME is superior to other methods such as principal components analysis, linear discriminant analysis, hidden Markov model, locally linear embedding and manifold-manifold distance.
作者 黄存东 刘仁金 杨思春
机构地区 安徽国防科技职业学院信息工程系 皖西学院信息工程学院 安徽工业大学计算机学院
出处 《计算机工程》 CAS CSCD 2012年第9期193-196,共4页 Computer Engineering
基金 安徽省高校教学研究基金资助重点项目(20101689) 安徽省自然科学基金资助项目(11040606M150) 安徽省高校自然科学研究基金资助重点项目(KJ2009A054 KJ2011A048)
关键词 特征融合 流形 视频人脸识别 局部特征 全局特征 feature fusion manifold video face recognition local feature global feature
分类号 TP391.41 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献13

  • 1陈财明,宋加涛,张石清.基于2DPCA和压缩感知的人脸识别方法[J].计算机工程,2011,37(22):176-178. 被引量：5
  • 2严严,章毓晋.基于视频的人脸识别研究进展[J].计算机学报,2009,32(5):878-886. 被引量：84
  • 3Kittler J,Hatef M,Duin R P W,et al.On Combining Classifiers[J].IEEE Trans.on Pattern Analysis and Machine Intelligence,1998,20(3):226-239.
  • 4Gross R,Shi J.The CMU Motion of Body(MoBo)Database[R].Robotics Institute,Carnegie Mellon University,Technical Report:CMU-RI-TR-01-18,2001.
  • 5Lee Kuang-Chih,Ho J M.Yang Ming-Hsuan,et al.Video-basedFace Recognition Using Probabilistic Appearance Manifolds[C]//Proc.of IEEE International Conference on Computer Vision andPattern Recognition.[S.l.]:IEEE Press,2003:313-320.
  • 6Cootes T F,Taylor C J.Active Shape Model——Their Trainingand Application[J].Computer Vision and Image Understanding,1995,61(1):38-59.
  • 7Ekenel H K,Stiefelhagen R.Local Appearance Based FaceRecognition Using Discrete Cosine Transform[C]//Proc.of the13th European Signal Processing Conference.Antalya,Turkey:[s.n.],2005:38-59.
  • 8David G.Distinctive Image Features from Scale-invariant InterestPoints[J].International Journal of Computer Vision,2004,60(2):91-110.
  • 9Snelick R,Uludag U,Mink A,et al.Large Scale Evaluation ofMultimodal Biometric Authentication Using State-of-the-artSystems[J].IEEE Trans.on Pattern Analysis and MachineIntelligence,2005,27(3):450-455.
  • 10Stallkamp J,Ekenel H K,Stiefelhagen R.Video-based FaceRecognition on Real-world Data[C]//Proc.of the 11th InternationalConference on Computer Vision.[S.l.]:IEEE Press,2007.

二级参考文献68

  • 1Chowdhury A, Chellappa R. Face reconstruction from monocular video using uncertainty analysis and a generic model.Computer Vision and Image Understanding, 2003, 91 (1) : 188-213
  • 2Choudhury A, Clarkson B, Jebara T, Penland A. Multimodal person recognition using unconstrained audio and video// Proceedings of the Conference on Audio- and Video-based Biometric Person Authentication. Washington D. C, 1999: 176-180
  • 3Zhang Z Y, Liu Z C, Adler D, Cohen M F, Hanson E, Shan Y. Robust and rapid generation of animated faces from video images: A model-based modeling approaeh. International Journal of Computer Vision, 2004, 58(2) : 93-119
  • 4Zhou X, Bhanu B. Integrating face and gait for human recognition at a distance in video. IEEE Transactions on Systems, Man and Cybernetics, Part B, 2007, 37(5):1119-1137
  • 5JingXY, Yao Y F, Zhang D, YangJ Y, Li M. Face and palmprint pixel level fusion and kernel DCV-RBF classifier for small sample biometric recognition. Pattern Recognition, 2007, 40(11): 3209-3324
  • 6Yan Y, Zhang Y J. Multimodal biometrics fusion using correlation filter bank//Proceedings of the 19th IAPR International Conference on Pattern Recognition. Tampa, 2008, MoBTT. 3(1-4)
  • 7McKenna S, Gong S, Raja Y. Face recognition in dynamic scenes//Proceedings of the British Machine Vision Conference. Colchester, 1997: 140-151
  • 8Park U, Jain A K, Ross A. Face recognition in video: Adaptive fusion of multiple matchers//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, 2007:1-8
  • 9Wolf L, Shashua A. Kernel principal angles for classification machines with applications to image sequence interpretation//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Madison, 2003:635-642
  • 10Fan W, Yeung D Y. Locally linear models on face appearance manifolds with application to dual-subspace based classification//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, 2006: 1384- 1390

共引文献87

同被引文献34

  • 1Lei Y,Bennamoun M,Hayat M,et al.An efficient 3D face recognition approach using local geometrical signatures[J].Pattern Recognition,2014,47(2):509-524.
  • 2Mohammadzade H,Hatzinakos D.Iterative closest normal point for 3D face recognition[J].Pattern Analysis and Machine Intelligence,IEEE Transactions on,2013,35(2):381-397.
  • 3Lei Y,Bennamoun M,El-Sallam A A.An efficient 3D face recognition approach based on the fusion of novel local low-level features[J].Pattern Recognition,2013,46(1):24-37.
  • 4Jain S,Bagga S,Hablani R,et al.Facial Expression Recognition Using Local Binary Patterns with Different Distance Measures[M]//Intelligent Computing,Networking,and Informatics.Springer India,2014:853-862.
  • 5Smeets D,Keustermans J,Vandermeulen D,et al.mesh SIFT:Local surface features for 3D face recognition under expression variations and partial data[J].Computer Vision and Image Understanding,2013,117(2):158-169.
  • 6Paris S,Glotin H,Zhao Z Q.Real-Time face detection using integral histogram of multi-scale local binary patterns[M]//Advanced Intelligent Computing.Springer Berlin Heidelberg,2012:276-281.
  • 7Bai T,Li Y F.Robust visual tracking with structured sparse representation appearance model[J].Pattern recognition,2012,45(6):2390-2404.
  • 8宫一.人像识别技术的发展与应用[J].中国人民公安大学学报（自然科学版）,2007,13(4):46-49. 被引量：14
  • 9龚卫国,桂祖宏,李正浩,辜小花.融合Adaboost和光流算法的视频人脸实时检测[J].仪器仪表学报,2008,29(7):1398-1402. 被引量：10
  • 10雷蕴奇,柳秀霞,宋晓冰,袁美玲,欧阳江帆.视频中运动人脸的检测与特征定位方法[J].华南理工大学学报（自然科学版）,2009,37(5):31-37. 被引量：6

引证文献5

二级引证文献9

计算机工程
2012年 第9期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部