A Novel Age Classification Method Using
Morph-Based Models
Asuman Günay
Yılmaz1 and Vasif Nabiyev2
1Department of Computer Technologies, Karadeniz
Technical University, Turkey
2Department of
Computer Engineering, Karadeniz Technical University, Turkey
Abstract: Automatic facial age
classification and estimation is an interesting and challenging problem, and
has many real world applications. The performances of the classification
methods may differ depending on the selected training samples. Also using large
amount of training samples makes the classification systems more complex and
time consuming. In this paper, a novel and a simple age classification method
using morph-based age models is presented. The age models representing the
common characteristics of age groups are produced using image morphing method. Then
age related facial features are extracted with Local Binary Patterns. In the
classification phase, ensemble of distance metrics is used to determine the
closeness of the test sample to age groups. Then, the results of these metrics
are combined with Borda Count voting method to improve the classification
performance. Experimental results using the Face and Gesture Recognition
Research Network (FGNET) and Park Aging Mind Laboratory (PAL) aging databases
show that the proposed method achieves better age classification accuracy when
compared to some of the previous methods.
Keywords: Age classification, image
morphing, local binary patterns, borda count voting.
Received January 27, 2016; accepted June 13, 2016