Principal Component Analysis for the Whole Facial Image With Pigmentation Separation and Application to the Prediction of Facial Images at Various Ages
In this article, principal component analysis is applied to pigmentation distribution in the whole face to obtain feature values, and the relationship between the obtained feature vectors and age is estimated by multiple regression analysis to simulate the changes of facial images in women of ages 10 to 80. Since the human face receives more attention than other body parts, a change of a small quantity of the features in a face makes a large difference to its appearance. We can divide the features into two categories. One category is physical features such as skin condition and shape, and the other is physiological features, which are influenced by age and health. In the beauty industry, the synthesis of skin texture is based on these two kinds of feature values. Previous works have analyzed only small areas of skin texture. By morphing the shapes of facial images to that of an average face and extending the analyzed area to the whole face, the authors’ method can analyze pigmentation distributions in the whole face and simulate the appearance of a face as a function of changing the person’s age.
Document Type: Research Article
Publication date: March 1, 2014
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