Authors:
Merve Kilinc
1
and
Yusuf Sinan Akgul
2
Affiliations:
1
TUBITAK BILGEM UEKAE, Turkey
;
2
Gebze Institute of Technology, Turkey
Keyword(s):
Age Estimation, Age Classification, Geometric Features, LBP, Gabor, LGBP, Cross Ratio, FGNET, MORPH.
Related
Ontology
Subjects/Areas/Topics:
Color and Texture Analyses
;
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
Abstract:
Aging progress of a person is influenced by many factors such as genetics, health, lifestyle, and even weather conditions. Therefore human age estimation from a face image is a challenging problem. Aging causes significant variations in facial shape and texture across years. In order to construct a general age classifier, shape and texture information of human face should be used together. In this paper, we propose a new age estimation system that uses a number of overlapping age groups and a classifier that combine geometric and textural facial features. The classifier scoring results are interpolated to produce the estimated age. We tested many geometric and textural facial features with age group classifiers. Comparative experiments show that the best performance is obtained using the fusion of Local Gabor Binary Patterns and Geometric features.