A Ranking Approach for Human Age Estimation Based on Face Images

Kuang-Yu Chang 1,3, Chu-Song Chen 1,2,4, and Yi-Ping Hung1,3,4

1 Institute of Information Science, Academia Sinica, Taipei, Taiwan.
2 Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan.
3 Dept. of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
4 Graduate Institute of Networking and Multimedia, National Taiwan University, Taipei, Taiwan
{kuangyu, song}@iis.sinica.edu.tw

 

Abstract

In our daily life, it is much easier to distinguishwhich person is elder between two persons than how old aperson is. When inferring a person’s age, we may compare his or her face with many people whose ages are known, resulting in a series of comparative results, and then we conjecture the age based on the comparisons. This process involves numerous pairwise preferences information obtained by a series of queries, where each query compares the target person’s face to those faces in a database. In this paper, we propose a ranking-based framework consisting of a set of binary queries. Each query collects a binary-classification-based comparison result. All the query results are then fused to predict the age. Experimental results show that our approach performs better than traditional multi-class-based and regression-based approaches for age estimation.

 

Publication

Kuang-Yu Chang, Chu-Song Chen, and Yi-Ping Hung, A Ranking Approach for Human Age Estimation Based on Face Images, International Conference on Pattern Recognition (ICPR), 2010.
[ Paper (606 KB) ] [ Bibtex ]

 

Method

We adapt the ordinal ranking approach, RED-SVM (Li and Lin, 2007), in our work. Some related publication and program can be found in Hsuan-Tien Lin's web site. [ Link ]

 

Datasets


We performed age estimation experiments on two benchmark age databases: (1) FG-NET and (2) MORPH Album 2, and use AAM as the feature extraction method.

FG-NET contains 1,002 color or gray facial images of 82 individuals with large variations in pose, expression and lighting. For each subject, there are more than ten images ranging from age 0 to age 69.

There are two scales of MORPH databases. We use the MORPH Album 2 that is a larger-scale database in our experiments. MORPH Album 2 contains 55,608 facial images with about three images per person ranging from 16 to 77 years old. To reduce the variation between ethnic groups, we selected 5,492 images of people of Caucasian descent, so that cross-race influence can be avoided.

Our related work (CVPR, 2011) use the same datasets.

Table 1. Age range distribution of face images in the FG-NET and the MORPH Album 2 databases.
Age Range FG-NET (%) MORPH (%)
0-9 37.03 0
10-19 33.83 8.94
20-29 14.37 26.04
30-39 7.88 32.16
40-49 4.59 24.58
50-59 1.5 7.37
60-69 0.8 0.82
70-77 0 0.09

 

Supplementary Files - Landmark of Morph Album 2

Copyright © 2011 Kuang-Yu Chang at Institute of Information Science, Academia Sinica.