The Library
Improving face-based age estimation with attention-based dynamic patch fusion
Tools
Wang, Haoyi, Sanchez Silva, Victor and Li, Chang-Tsun (2022) Improving face-based age estimation with attention-based dynamic patch fusion. IEEE Transactions on Image Processing, 31 . pp. 1084-1096. doi:10.1109/TIP.2021.3139226 ISSN 1057-7149.
|
PDF
WRAP-Improving-face-based-age-estimation-dynamic-patch-fusion-2022.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (2168Kb) | Preview |
Official URL: https://doi.org/10.1109/TIP.2021.3139226
Abstract
With the increasing popularity of convolutional neural networks (CNNs), recent works on face-based age estimation employ these networks as the backbone. However, state-of-the-art CNN-based methods treat each facial region equally, thus entirely ignoring the importance of some facial patches that may contain rich age-specific information. In this paper, we propose a face-based age estimation framework, called Attention-based Dynamic Patch Fusion (ADPF). In ADPF, two separate CNNs are implemented, namely the AttentionNet and the FusionNet. The AttentionNet dynamically locates and ranks age-specific patches by employing a novel Ranking-guided Multi-Head Hybrid Attention (RMHHA) mechanism. The FusionNet uses the discovered patches along with the facial image to predict the age of the subject. Since the proposed RMHHA mechanism ranks the discovered patches based on their importance, the length of the learning path of each patch in the FusionNet is proportional to the amount of information it carries (the longer, the more important). ADPF also introduces a novel diversity loss to guide the training of the AttentionNet and reduce the overlap among patches so that the diverse and important patches are discovered. Through extensive experiments, we show that our proposed framework outperforms state-of-the-art methods on several age estimation benchmark datasets.
Item Type: | Journal Article | ||||||
---|---|---|---|---|---|---|---|
Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General) |
||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
Library of Congress Subject Headings (LCSH): | Human beings -- Age determination, Image processing -- Digital techniques, Neural networks (Computer science), Face -- Aging | ||||||
Journal or Publication Title: | IEEE Transactions on Image Processing | ||||||
Publisher: | IEEE | ||||||
ISSN: | 1057-7149 | ||||||
Official Date: | 6 January 2022 | ||||||
Dates: |
|
||||||
Volume: | 31 | ||||||
Page Range: | pp. 1084-1096 | ||||||
DOI: | 10.1109/TIP.2021.3139226 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Date of first compliant deposit: | 2 March 2022 | ||||||
Date of first compliant Open Access: | 2 March 2022 | ||||||
RIOXX Funder/Project Grant: |
|
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year