Name
Abstract | ||
|---|---|---|
In this paper, a deep Convolutional Neural Network CNN based system, called Depthwise Separable Convolutional Neural Network (DSCNN) fusion system, for human facial age estimation is presented. This system includes following four stages. In the first stage, a data augmentation procedure is utilized to enrich the dataset. In the second stage, a pre-trained deep CNN model is fine-tuned for the gender classification task. For the third stage, three newly designed DSCNN age estimators are utilized to conduct gender-specific age estimation for gender grouped facial images from previous stage. The architectures of these three deep DSCNNs are constructed to lower computation complexity. In the last stage, estimated ages from three DSCNN age estimators are fed to the fuser to boost the overall age estimation performance. In the experimental results, on four benchmark datasets, IMDB-WIKI, MORPH-II, and ChaLearn LAP Apparent age V1 and V2, the proposed system demonstrates a significant performance improvement over the state-of-the-art deep CNN models and methods. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/WIFS.2018.8630776 | 2018 IEEE International Workshop on Information Forensics and Security (WIFS) |
Keywords | Field | DocType |
human facial age estimation,data augmentation procedure,gender classification task,gender-specific age estimation,deep DSCNNs,age estimation performance,ChaLearn LAP Apparent age,DSCNN age estimators,pretrained deep CNN model,depthwise separable convolutional neural network fusion system,deep convolutional neural network CNN based system | Computer vision,Pattern recognition,Computer science,Convolutional neural network,Augmentation procedure,Separable space,Fusion,Apparent age,Artificial intelligence,Computation complexity,Performance improvement,Estimator | Conference |
ISSN | ISBN | Citations |
2157-4766 | 978-1-5386-6537-4 | 0 |
PageRank | References | Authors |
0.34 | 20 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kuan-Hsien Liu | 1 | 110 | 11.01 |
| Hsin-Hua Liu | 2 | 27 | 5.68 |
| Pak Ki Chan | 3 | 0 | 0.34 |
| Tsung-Jung Liu | 4 | 147 | 13.20 |
| Soo-Chang Pei | 5 | 2054 | 241.11 |