The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Joint Age Estimation and Gender Classification of Asian Faces Using Wide ResNet
Open accessAbstract
Two key facial features, age and gender, have been widely explored. Companies and organizations have investigated in related applications in several fields including insurance, retails, marketing, etc. It would bring tremendous benefit, which allow companies to easily identify their customer demographics. Several approaches have been proposed with remarkable results. However, because of the lack of open and multi-ethnic datasets, most modern age and gender estimating models were trained solely based on white people with Western facial features, and thus fall short with non-Caucasian people. In this paper, we developed an applicable Wide ResNet model to estimate the age and the gender of Asian faces. The model was trained with a newly improved Asian face database. The experiments have shown promising results, as it can match the performance of Microsoft's how-old API estimator in a specific dataset.
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Inserting a dropout between the convolutional layers (as opposed to within the residual block) made the learning more effective in WideResNet.
“However, inserting a dropout between the convolutional layers (as opposed to within the residual block) made the learning more effective in WideResNet [121, 122].”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [121]
The Proposed Method’s “Wide ResNet Architecture” section places dropout between convolutional blocks and says it gives the model more room for generalization and helps prevent overfitting. It does not show that this placement made learning more effective than placing dropout within a residual block. (LLM, conf=0.83)
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Wide Residual NetworksPreprint
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Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet ClassificationPreprint
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Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks
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Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00444-8 · Cited by 7,906
Metadata
- Authors
- Huynh, H. T., & Nguyen, H.
- Affiliations
- Industrial University of Ho Chi Minh City (VN)
- Vietnamese-German University (VN)
- Corresponding author
- Huynh, Hieu Trung
- Venue
- SN Computer Science
- Published
- 2020-08-27
- Volume
- 1
- Issue
- 5
- Pages
- 284-284
- ISSN
- 2661-8907 (ISSN-L)
- 2662-995X
- PMC
- PMC7451232
- OpenAlex
- W3082383500
- Cited by
- 20
- References
- 19
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 1 · 2023: 5 · 2022: 6 · 2021: 3
- FWCI
- 1.36
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- SN Computer Science (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Social Sciences › Arts and Humanities › Archeology (arts and humanities) › Forensic Anthropology and Bioarchaeology Studies
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- age estimation, gender classification, Asian faces, WideResNet, facial analysis
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