Paper Info
Title
Age prediction using a supervised facial model.
Abstract
Facial rejuvenation has driven a lot of research in the field of dermatology and plastic surgery, leading to many medical procedures. This paper proposes an age prediction method that could be used to better understand the ageing process and to evaluate the benefits of a rejuvenating treatment, for example. A supervised Facial Model (SFM) is built using Partial Least Squares regression (PLSR) to capture and summarize age related changes from a database of front face images. The model describes the changes related to the shape and proportions of facial features, color and texture of the face. Experimental results from a database of 173 Caucasian women pictures demonstrate that the model matches human perception.
Year
DOI
Venue
2011
10.1109/ISBI.2011.5872613
ISBI
Keywords
Field
DocType
face recognition,feature extraction,image colour analysis,image representation,image texture,learning (artificial intelligence),least squares approximations,medical image processing,regression analysis,skin,surgery,Caucasian women,age prediction,dermatology,facial features,facial rejuvenation,facial representation,front-face image database,image color,image texture,partial least squares regression,plastic surgery,rejuvenating treatment,supervised facial model,PLS,age prediction,aging,face
Facial recognition system,Computer vision,Pattern recognition,Image texture,Regression analysis,Computer science,Partial least squares regression,Feature extraction,Artificial intelligence,Pixel,Facial rejuvenation,Perception
Conference
ISSN
Citations 
PageRank 
1945-7928
0
0.34
References 
Authors
6
3
Name
Order
Citations
PageRank
Alex Nkengne111.11
Arthur Tenenhaus2648.61
Bernard Fertil39713.32