ORCID
- Haoyi Wang: 0000-0001-6465-8096
- Nathan Clarke: 0000-0002-3595-3800
Abstract
Generalized age feature extraction is crucial for age-related facial analysis tasks, such as age estimation and age-invariant face recognition (AIFR). Despite the recent successes of models in homogeneous-dataset experiments, their performance drops significantly in cross-dataset evaluations. Most of these models fail to extract generalized age features as they only attempt to map extracted features with training age labels directly without explicitly modeling the natural ordinal progression of aging. In this paper, we propose Order-Enhanced Contrastive Learning (OrdCon), a novel contrastive learning framework designed explicitly for ordinal attributes like age. Specifically, to extract generalized features, OrdCon aligns the direction vector of two features with either the natural aging direction or its reverse to model the ordinal process of aging. To further enhance generalizability, OrdCon leverages a novel soft proxy matching loss as a second contrastive objective, ensuring that features are positioned around the center of each age cluster with minimal intra-class variance and proportionally away from other clusters. By modeling the ageing process, the framework can enhance generalizability by improving the alignment of samples from the same class and reducing the divergence of direction vectors. We demonstrate that our proposed method achieves comparable results to state-of-the-art methods on various benchmark datasets in homogeneous-dataset evaluations for both age estimation and AIFR. In cross-dataset experiments, OrdCon outperforms other methods by reducing the mean absolute error by approximately 1.38 on average for the age estimation task and boosts the average accuracy for AIFR by 1.87%.
DOI Link
Publication Date
2025-08-08
Publication Title
IEEE Transactions on Information Forensics and Security
Volume
20
ISSN
1556-6013
Acceptance Date
2025-07-30
Deposit Date
2025-08-03
Funding
This work was supported by the European Union (EU) Horizon 2020-Marie Sklodowska-Curie Actions through the Project Computer Vision Enabled Multimedia Forensics and People Identification under Project 690907.
Additional Links
Keywords
Age estimation, age-invariant face recognition, biometrics, contrastive learning, metric learning, order learning
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
First Page
8525
Last Page
8540
Recommended Citation
Wang, H., Sanchez, V., Li, C., & Clarke, N. (2025) 'From Age Estimation to Age-Invariant Face Recognition: Generalized Age Feature Extraction Using Order-Enhanced Contrastive Learning', IEEE Transactions on Information Forensics and Security, 20, pp. 8525-8540. Available at: 10.1109/TIFS.2025.3597187
