Paper Info
Title
Facial feature fusion and model selection for age estimation
Abstract
Automatic face age estimation is challenging due to its complexity owing to genetic difference, behavior and environmental factors, the dynamics of facial aging between different individuals, etc. In this work we propose to fuse the global facial feature extracted from Active Appearance Model (AAM) and the local facial features extracted from Local Binary Pattern (LBP), as the representation of faces. Furthermore, we introduce an advanced age estimation system combining feature fusion and model selection schemes such as Least Angle Regression (LAR) and sequential approaches. Due to the fact that different facial feature representations may come with various types of measurement scales, we compare multiple normalization schemes for both facial features. We demonstrate that the feature fusion with model selection can achieve significant improvement in age estimation over single feature representation alone. Our experiment on multi-ethnicity UIUC-PAL database suggests that age estimation with feature fusion and model selection outperforms the single feature, or the full feature model.
Year
DOI
Venue
2011
10.1109/FG.2011.5771398
FG
Keywords
Field
DocType
model selection scheme,active appearance model,global facial feature extraction,regression analysis,local binary pattern,visual databases,least angle regression,automatic face age estimation,feature extraction,facial feature fusion,local facial features extraction,multiethnicity uiuc-pal database,databases,genetics,aging,model selection
Normalization (statistics),Pattern recognition,Computer science,Feature (computer vision),Local binary patterns,Model selection,Active appearance model,Feature extraction,Feature model,Artificial intelligence,Least-angle regression
Conference
ISBN
Citations 
PageRank 
978-1-4244-9140-7
7
0.45
References 
Authors
7
5
Name
Order
Citations
PageRank
Cuixian Chen1536.38
Wankou Yang253534.68
Yishi Wang3435.50
Karl Ricanek416518.65
Khoa Luu520026.05