Name
Abstract | ||
|---|---|---|
This paper was aimed to address the problem of image-based human age estimation. It has the following main contributions. First, we provide a comparison of three hand-crafted image features and five deep convolutional neural networks (DCNNs). Secondly, we show that the use of pre-trained DCNNs as feature extractors can transfer the knowledge of DCNNs to new datasets and domains that were not necessarily addressed in the training phase. This is achieved by only retraining a shallow regressor over the deep features. Thirdly, we provide a cross-database evaluation involving biological and apparent ages. The paper shows that transfer learning allows the use of pre-trained DCNNs regardless of the type of ages (apparent or biological) that is adopted in DCNN training. The experiments are carried out on three public databases: MORPH, PAL, and Chalearn2016. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s00138-018-0976-1 | Mach. Vis. Appl. |
Keywords | Field | DocType |
Age estimation, Deep CNNs, Transfer learning, Regression, Cross-database evaluation, Mean Absolute Error | Regression,Pattern recognition,Feature (computer vision),Convolutional neural network,Computer science,Transfer of learning,Mean absolute error,Artificial intelligence,Retraining | Journal |
Volume | Issue | ISSN |
30 | 1 | 1432-1769 |
Citations | PageRank | References |
0 | 0.34 | 27 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fadi Dornaika | 1 | 809 | 96.43 |
| Ignacio Arganda-Carreras | 2 | 71 | 8.02 |
| C. Belver | 3 | 0 | 0.34 |