Name
Abstract | ||
|---|---|---|
Different from the traditional age estimation systems, this paper introduces a novel method to estimate human age from facial images combined with non-fixed age group classification and Rank-based age value estimation. The basic idea is to use the continuous and ordinal information from human facial aging process. Firstly, pair-wise distance classifiers are employed to roughly estimate age within total database. Then a flexible and more precise age group is decided by extending the rough age value to a specific age scope. For the ranking framework of precious age estimation, by calculating the continental distances to select the most similar sample from the training database. The average label of the voted samples is used to predict age. In our proposed system, six-dimension shape features and different texture features are used to describe facial images. Tested on FG-NET database, our system achieves 4.89 evaluated by MAE (Mean Absolute Error). |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2556871.2556891 | ICCC |
Keywords | Field | DocType |
facial images,new method,fg-net database,hierarchical model,traditional age estimation system,specific age scope,precise age group,non-fixed age group classification,human age,age estimation,facial image,rank-based age value estimation,rough age value,precious age estimation,ranking | Data mining,Ranking,Pattern recognition,Ordinal number,Computer science,Mean absolute error,Artificial intelligence,Hierarchical database model | Conference |
Citations | PageRank | References |
3 | 0.39 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li Zhang | 1 | 93 | 13.90 |
| Xianmei Wang | 2 | 3 | 3.09 |
| Yuyu Liang | 3 | 3 | 1.40 |
| Lun Xie | 4 | 27 | 10.06 |