Cited by 91Face recognition and analysisAdvanced Neural Network ApplicationsForensic Anthropology and Bioarchaeology Studies

Deep Differentiable Random Forests for Age Estimation

Shen, Wei, Guo, Yilu, Wang, Yan, Zhao, Kai, Wang, Bo, Yuille, Alan·2021·IEEE Transactions on Pattern Analysis and Machine Intelligence

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.

Integrity signals

Registry

Crossref · 10.1109/tpami.2019.2937294

Cites

59 of 59 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Cites

Flagged first, then by citations

1 of the 60 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

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
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review