基于主成分分析的唇部轮廓建模

王丽荣; 王建蕾

doi:10.3788/OPE.20122012.2768

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基于主成分分析的唇部轮廓建模

信息科学 | 更新时间：2020-08-13

- 基于主成分分析的唇部轮廓建模
- Lip contour modeling based on PCA
- 光学精密工程 2012年20卷第12期页码：2768-2772
- 作者机构：
  
  1. 长春大学电子信息工程学院,吉林长春,130022
  2. 长春理工大学电子信息工程学院,吉林长春,130022
- 作者简介：
- 基金信息：
  
  吉林省科技厅自然科学基金资助项目(No.20101518)();教育部重点项目(No.201049)()
- DOI：10.3788/OPE.20122012.2768
  中图分类号： TP391
- 收稿日期：2012-07-17，
  
  修回日期：2012-09-25，
  
  纸质出版日期：2012-12-10
- 稿件说明：
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王丽荣, 王建蕾. 基于主成分分析的唇部轮廓建模[J]. 光学精密工程, 2012,20(12): 2768-2772

WANG Li-rong, WANG Jian-lei. Lip contour modeling based on PCA[J]. Editorial Office of Optics and Precision Engineering, 2012,20(12): 2768-2772
王丽荣, 王建蕾. 基于主成分分析的唇部轮廓建模[J]. 光学精密工程, 2012,20(12): 2768-2772 DOI： 10.3788/OPE.20122012.2768.

WANG Li-rong, WANG Jian-lei. Lip contour modeling based on PCA[J]. Editorial Office of Optics and Precision Engineering, 2012,20(12): 2768-2772 DOI： 10.3788/OPE.20122012.2768.

摘要

研究了基于主成分分析(PCA)的唇部轮廓建模方法。首先

对5 000个样本唇部轮廓进行标定并对标定的坐标数据进行Procrustes分析

使数据归一化。然后

通过PCA算法寻找形变模式

在保持形变范围内最大限度地降低数据维数并利用所得到的均值和特征向量构建唇部轮廓模型。最后

利用PCA得到的前16种模式所建立的模型对5 000个样本原始的唇部轮廓进行重构。实验结果显示:PCA得到的前4种模式分别描述了唇部角度、下唇、尺度以及唇角等的形变信息

其余模式描述了唇部更细致的形变

模型重构的唇部轮廓与相应样本原始唇部轮廓的每个特征点之间平均差异均不大于0.6个像素宽。结果表明所建唇部模型能满足特征定位精度要求。

Abstract

A modeling method of lip contours was proposed based on Principle Component Analysis (PCA). Firstly

the lip contours of 5 000 training samples were labeled

and the Procrustes analysis was performed on the coordinates gotten by labeling to normalize the data. Then the PCA was used to identify modes in data and compress the data dimension without losing the lip contour information.Furthermore

the lip contour model was constructed by using a mean value and eigenvectors gotten by PCA. Finally

the model constructed by the first 16 modes gotten by PCA was taken to reconstruct the lip contours of the original 5 000 samples. Experimental results indicate that the first 4 modes respectively describe the rotation

lower lip

scale and corners of the lip

and other modes describe more detailed lip variation.The mean difference of every feature point between the lip contour gotten by model reconstruction and the original contour is less than a width of 0.6 pixels. The model can satisfy the precision requirements of the feature location.

关键词

Keywords

references

RONG L W, GUANG X Y, LEI J W,et al.. Facial expression recognition based on local texture features. 2011 IEEE 14th International Conference on Computational Science and Engineering, 2011:543-546.[2] 吴本涛,吴敏渊,曾霖. 自适应搜索的快速分块跟踪[J]. 光学精密工程,2011,19(3):703-708. WU B T, WU M Y,ZENG L. Fast fragment based tracking using adaptive search[J]. Opt. Precision Eng., 2011,19(3):703-708.(in Chinese)[3] 赵燕燕,基于视频图像的唇部检测与跟踪方法研究. 长春:长春理工大学,2008. ZHAO Y Y. Research on the methods of lip detecting and tracking based on video image. Changchun:Changchun University of Science and Technology,2008.(in Chinese)[4] 王国良,刘金国. 基于粒子滤波的多自由度运动目标跟踪[J]. 光学精密工程,2011,19(4):864-869. WANG G L,LIU J G. Moving object tracking with multi-degree-of-freedom based on particle filters[J]. Opt. Precision Eng., 2011,19(4):864-869. (in Chinese)[5] 颜佳,吴敏渊. 遮挡环境下采用在线Boosting的目标跟踪[J]. 光学精密工程,2012,20(2):439-446. YAN J,WU M Y. On-line boosting based target tracking under occlusion[J]. Opt. Precision Eng., 2012,20(2):439-446.(in Chinese)[6] YUILLE A L,HALLINAN P,COLEN D S. Feature extraction from faces using deformable templates[J].International Journal of Computer Vision.1992, 8(2):99-111.[7] DAVATZIKOS C A,PRINCE J L. An active contour model for mapping the cortex[J].IEEE Transactions on Medical Imaging,1995,14(1):65-80.[8] COOTES T F,TAYLOR C J,COOPER D H,et al.. Active shape model-their training and application[J].Computer Vision and Image Understanding,1995, 61(1):38-59.[9] CHEN S Y, ZHANG J. Detection and amendment of shape distortions based on moment invariants for active shape models[J].Image Processing,IET,2011,5(3):273-285. [10] LUETTIN J. Visual speech and speaker recognition. University of Sheffield,1997:41-70.[11] http://www.isbe.man.ac.uk/～bim/data/talking_face/talking_face.html.[12] GOWER J C. Generalized procrustes analysis[J]. Psychometrika, 1975,40(1):33-51.[13] JOLLIFFER I T. Principal Component Analysis: A Beginner's Guide[M]. Wiley Online Library,2012.

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