Dual-Subspaces based Quantitative Analysis of Facial Appearance
We propose a subspace based method for quantitative analysis of facial appearance. We first collect both natural and cosmetic female facial images and then construct a subspace of female facial images using principal component analysis (PCA). We divide the subspace into two subspaces:one is global subspace which is constructed by eigenvectors with larger eigenvalues and another one is local subspace which is constructed by eigenvectors with smaller eigenvalues.Both natural and cosmetic facial images are projected to global subspace and local subspace, respectively. The difference (distance) of the projection between the natural facial image and cosmetic facial image is used as a quantitative measure of make-up effect. We found that the difference (distance ) in global subspace represents skin color information and the difference (distance) in local subspace represents skin texture information. The quantitative analysis results are strongly correlated with the results of psychological test.
principal component analysis (PCA) eigenface skin natural facial image cosmetic facial image dual-subspaces psychological test MaVIC Quantitative Analysis of facial appearance
Junji Moriguchi Takanori Igarashi Keisuke Nakao Yen-Wei Chen
Graduate School of Science and Engineering, Ritsumeikan University, Shiga, Japan Beauty Cosmetic Research Lab. Kao Corporation. Tokyo, Japan
国际会议
成都
英文
620-624
2010-06-23（万方平台首次上网日期，不代表论文的发表时间）