[1]张一鸣,欧宗瑛,王 虹.基于C均值K近邻算法的面部表情识别[J].智能系统学报,2008,3(01):57-61.
 ZHANG Yi-ming,OU Zong-ying,WANG Hong.Facial expression recognition based on Cmeans and K-nearest neighbor algorithms[J].CAAI Transactions on Intelligent Systems,2008,3(01):57-61.
点击复制

基于C均值K近邻算法的面部表情识别(/HTML)
分享到:

《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第3卷
期数:
2008年01期
页码:
57-61
栏目:
出版日期:
2008-02-25

文章信息/Info

Title:
Facial expression recognition based on Cmeans and K-nearest neighbor algorithms
文章编号:
1673-4785(2008)01-0057-05
作者:
张一鸣欧宗瑛王 虹
大连理工大学机械工程学院,辽宁大连116023
Author(s):
ZHANG Yi-ming OU Zong-ying WANG Hong 
School of Mechanical Engineering, Dalian University of Technol ogy,Dalian 116023,China
关键词:
面部表情识别C均值聚类K近邻Gabor小波Fisherface判别分析
Keywords:
facial expression recognition Cmeans clusterin g K-nearest neighbor Gabor wavelet fisherface discriminant analysis
分类号:
TP39141
文献标志码:
A
摘要:
随着人工智能与模式识别技术的不断发展,面部表情识别在智能人机交互中发挥着越来越重要的作用.通过对人的面部表情分类的研究,提出了一种使用C均值聚类、K近邻算法的面部表情分类方法.对参加训练的表情图像先进行Gabor小波变换,然后使用Fisherface判别分析方法进行变换,求得特征空间.再将已进行Gabor变换的标准表情图像投影到特征空间,进行C均值聚类得到子类表情模板.对于一幅待识别的表情图像,使用K近邻算法与子类表情模板比较,将该表情图像分类.使用该方法,在公开的日本女人表情人脸库上实测达到了95 8%的识别率.
Abstract:
With the rapid development of artificial intelligence and pattern rec ognition, facial expression recognition plays an important role in intelligent humanmachine interaction. In this paper, a facial expression classific a tion method is presented which uses a Cmeans and Knearest neighbor a lgorithm a s the basis of analysis for the classification of facial expressions. First the images to be analyzed are transformed with Gabor wavelets, and then Fisherface d i scriminate analysis is performed to generate a feature space. Next, the images w hich were transformed with Gabor wavelets are projected into the feature space a n d Cmeans clustering performed on the projected images to generate subexp ress io n templates. Finally, the type of expression is identified by comparing the inpu t expression images with the subexpression templates by using a Knearest nei gh bor algorithm. Experiments on the public Japanese female facial expression datab as e show that the method proposed in this paper can achieve a 95.8% recognition rate. 

参考文献/References:

[1]EKMAN P, FRIESEN W V. Facial action coding system: a technique for t he measurement of facial movement [M]. Palo Alto, CA:Consulting Psychologists Press,1978.
[2]MASE K. Recognition of facial expression from optical flow [J]. IEICE Tr ans E,1991,74(10):34743483.
[3]YACOOB Y, DAVIS L. Recognizing human facial expressions from long image se quences using optical flow [J]. IEEE Trans on PAMI,1996,18(6):636642.
[4]COTTRELL G,METCALFE J. Face, gender and emotion recognition using Holons [C]// Advances in Neural Information Processing Systems. Denver, USA, 1990,3: 564571.
[5]PADGETT C, COTTRELL G. Representing face images for emotion classificatio n[C]// Advances in Neural Informati on Processing Systems. Cambridge: MIT Press,1997.
[6]金 一, 阮秋琦.一种局部加权的二维主成分分析算法及其在人脸识别中的应用[J].智能系统学报,2007,2(3):2529.
JIN Yi,RUAN Qiuqi.A part weighted twodimensional PCA for face recognition[J] .CAAI Transactions on Intelligent Systems, 2007,2(3):2529.
[7]DONATO G,STEWART M B, HAGER J C, et al. Classifying facial acti ons [J]. IEEE Trans on PAMI, 1999, 21(10): 974989.
[8]DAUGMAN J G. Complete discrete 2D Gabor transform by neural networks for image analysis and compression [J]. IEEE Trans on ASSP,1998, 36(7): 1169117 9 .
[9]BUCIU I, KOTROPOULOS C.ICA and Gabor representation for facial ex pression recognition[C]// Proceedings of IEEE ICIP. Barcelona, Spain, 2003.
[10]PENEV P S, ATICK J J. Local feature analysis: a general statistica l theor y for object representation [J]. Network: Computation in Neural Systems, 1996 ,7(3):477500.
[11]CALDER A J, BURTON A M, MILLER P, et al. A principal component analysis o f facial expressions [J].Vision Research, 2001,41(9): 11791208.
[12]SEYEDARABI H, AGHAGOLZADEH A, KHAN MOHAMMADI S. Recognition of six basic f acial expressions by featurepoints tracking using RBF neural network and fuzzy inference system[C]// Proceedings of 2004 IEEE International Conference on Mu ltimedia and Expo. Taipei, China, 2004.
[13]MA L,KHORASANI K. Facial expression recognition using constructive feedf orward neural networks [J]. IEEE Trans on SMCPart B,2004,34(3):15881595 . 
[14]LADES M,JAN C. Distortion invariant object recognition in the dynamic li nk architecture [J]. IEEE Trans on Computer,1993, 42(3):300311.
[15]边肇祺,张学工.模式识别[M].北京:清华大学出版社,2000.
[16]LYONS M J,BUDYNEK J,AKAMATSU S. Automatic classification of single faci al images [J]. IEEE Trans on PAMI,1999,21(12):13571362.

备注/Memo

备注/Memo:
收稿日期:2007-02-10.
基金项目:大连理工大学与中科院沈阳自动化研究所联合探索基金资助项目(DUTSIA 2006).
作者简介:
张一鸣,女,1981年生,硕士研究生,主要研究方向为模式识别.
欧宗瑛,男,1936年生,教授,博士生导师,主要研究方向为计算机辅助设计、计算机图形学和图像处理.获教委和机械部科技三等奖各一项,辽宁省科技一等奖一项.参与主编的机械设计手册和机电类规划教材分别获1995全国科技图书二等奖和2002全国优秀教材二等奖. 发表的学术论文被SCI检索12篇,被EI检索95篇.
王 虹,男,1964年生,博士研究生,主要研究方向为模式识别、图像处理
通讯作者:欧宗瑛.E-mail:ouzyg@dlut.edu.cn.
更新日期/Last Update: 2009-05-10