Deep Differentiable Random Forests for Age Estimation
Abstract
Age estimation from facial images is typically cast as a label distribution learning or regression problem, since aging is a gradual progress. Its main challenge is the facial feature space w.r.t. ages is inhomogeneous, due to the large variation in facial appearance across different persons of the same age and the non-stationary property of aging. In this paper, we propose two Deep Differentiable Random Forests methods, Deep Label Distribution Learning Forest (DLDLF) and Deep Regression Forest (DRF), for age estimation. Both of them connect split nodes to the top layer of convolutional neural networks (CNNs) and deal with inhomogeneous data by jointly learning input-dependent data partitions at the split nodes and age distributions at the leaf nodes. This joint learning follows an alternating strategy: (1) Fixing the leaf nodes and optimizing the split nodes and the CNN parameters by Back-propagation; (2) Fixing the split nodes and optimizing the leaf nodes by Variational Bounding. Two Deterministic Annealing processes are introduced into the learning of the split and leaf nodes, respectively, to avoid poor local optima and obtain better estimates of tree parameters free of initial values. Experimental results show that DLDLF and DRF achieve state-of-the-art performance on three age estimation datasets.
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- Authors
- Shen, W., Guo, Y., Wang, Y., Zhao, K., Wang, B., & Yuille, A.
- Affiliations
- Johns Hopkins University (US)
- Shanghai University (CN)
- Nankai University (CN)
- University Health Network (CA)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2021-02-01
- Volume
- 43
- Issue
- 2
- Pages
- 404-419
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W2971130703
- Cited by
- 91
- References
- 60
- Citations by year
- 2026: 6 · 2025: 15 · 2024: 18 · 2023: 18 · 2022: 15 · 2021: 12 · 2020: 7
- FWCI
- 3.81
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (61672336)
- ONR (N00014-15-1-2356)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Social Sciences › Arts and Humanities › Archeology › Forensic Anthropology and Bioarchaeology Studies
- Keywords
- age estimation, face images, label distribution learning, regression forests, convolutional neural networks, deterministic annealing
- MeSH terms
- Algorithms, Face, Learning, Neural Networks, Computer
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