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REVIEW 3 major objections 4 minor 66 references

Bridging the gap in FER: addressing age bias in deep learning

T0 review · 3 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read The paper claims age-related bias in facial expression recognition can be substantially reduced by simple training changes, with Age-weighted Loss raising elderly macro F1 on FACES from 0.7528 to 0.8140 and cutting the adult–elderly gap…

desk verdict Worth refereeing, but the central mitigation claim rests on 114 elderly test images and no significance tests; treat the elderly gains as suggestive, not established. read the letter →

arxiv 2507.07638 v1 pith:NI2GIR2C submitted 2025-07-10 cs.CV

F. Xavier Gaya-Morey , Julia Sanchez-Perez , Cristina Manresa-Yee , Jose M. Buades-Rubio This is my paper · ORCID
classification cs.CV
keywords facialexpressionrecognitionagebiaselderlymitigationage-weightedlossmulti-tasklearningexplainableAIAffectNet
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that age-related bias in facial expression recognition, especially the poor recognition of elderly faces, can be substantially reduced without redesigning the neural network. Using AffectNet with automatically estimated age labels, the authors identify which expressions suffer most for elderly people (neutral, sadness, anger) and test three simple fixes: reweighting the training loss by age group, adding age prediction as a secondary task, and feeding age to the classifier. The strongest results come from Age-weighted Loss, which raises the elderly macro F1 on the FACES benchmark from 0.7528 to 0.8140 and cuts the adult–elderly performance gap from 0.0917 to 0.0380. The broader point is that even noisy, automatically estimated demographic labels are enough to steer a model toward fairer behavior across age groups.

What carries the argument

The load-bearing mechanism is the Age-weighted Loss, a per-sample weight $w_{s,a} = N_{\min}/N_{s,a}$ that rebalances the joint distribution of expression classes and age groups during training, making underrepresented (expression, age) pairs contribute proportionally more to the gradient. The paper's diagnosis uses saliency heatmaps aggregated per expression and age group, computed with single-pass saliency maps rather than the more expensive LIME, to show that the baseline attends to largely overlapping facial regions for neutral, sadness, and anger—exactly the confused classes in elderly faces—and that the improved model shifts attention for sadness and happiness. The other two strategies, multi-task learning with a parallel age-regression head and multi-modal input that concatenates the age scalar to a downsampled visual feature vector, are alternative routes to injecting age information, but the weighted loss produces the largest elderly gains.

What would settle it

Run the best Age-weighted Loss model on an independent, larger elderly dataset with all seven expressions and manual age labels; if the adult–elderly macro-F1 gap does not shrink by roughly the same margin, the claimed mitigation is specific to the FACES subset rather than to age bias itself.

Watch

Extended reading notes

Core claim

The central claim is that age-related bias in deep FER originates mainly in training-data representation rather than in architecture, so simple training modifications can substantially reduce it. On AffectNet, with age labels automatically estimated by MiVOLO, the paper trains MobileNetV3, Swin Transformer, and ConvNeXt-Base in four configurations and finds that all three mitigation strategies—Age-weighted Loss, Multi-task Learning, and Multi-modal Input—improve recognition for elderly faces overall. The strongest result is ConvNeXt-Base with Age-weighted Loss: elderly macro F1 on the FACES benchmark rises from 0.7528 to 0.8140, and the adult–elderly gap falls from 0.0917 to 0.0380. Confusion-matrix analysis shows the main gains come from resolving elderly neutral–anger–sadness confusions and the sadness–disgust confusion, and saliency heatmaps show the age-aware model shifting attention to more informative facial regions for elderly faces, such as the brow and mouth edges for sadness. The paper reads this as evidence that even approximate, automatically generated demographic labels are enough to make large-scale FER training fairer across age groups.

Load-bearing premise

The entire elderly-side conclusion rests on a single small benchmark: the FACES elderly subset contains 114 images and no surprise expressions, so the measured bias reduction may not transfer to elderly faces more broadly.

Editorial extensions

If this is right

  • Adding Age-weighted Loss to a standard FER training pipeline improves elderly recognition without changing the network architecture, and it combines with both multi-task and multi-modal age-injection strategies.
  • The adult–elderly macro-F1 gap on FACES drops from 0.0917 to 0.0380 for ConvNeXt-Base, and elderly macro F1 rises from 0.7528 to 0.8140, while overall performance across age groups stays roughly stable.
  • The elderly misclassifications that most hurt fairness—neutral read as anger or sadness, and sadness read as disgust—are the ones that shrink most under age-aware training.
  • Models trained on AffectNet with automatically estimated age labels outperform a specialized age-group-aware method [31] on the elderly FACES subset, suggesting that dataset diversity and simple reweighting can rival bespoke architectures.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A testable extension the paper leaves open is to feed the multi-task model's own age estimate back as the age input at inference; if the elderly gains persist, the approach stops depending on external age labels entirely.
  • The children's fear–surprise confusion survives all three mitigations, which suggests this particular bias may be driven by how the expressions are labeled or expressed rather than by training-set imbalance; re-annotating children's faces with adult labels would test that.
  • If rebalancing is the active mechanism, the same Age-weighted Loss should shrink performance gaps for other rare demographic groups, such as specific ethnicities or gender groups, on the same benchmark suite.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper studies age-related bias in deep facial expression recognition (FER) models, with a focus on elderly subjects. Using AffectNet as the training set with automatically estimated (MiVOLO) age labels, the authors evaluate three model-agnostic mitigation strategies: Age-weighted Loss (Eq. 1), Multi-task Learning with age regression, and Multi-modal Input with age concatenated to the feature vector. Evaluation is carried out on balanced, lab-controlled test sets for children, adults, and elderly (the latter exclusively from FACES, 114 images). The paper reports that Age-weighted Loss, in particular, raises elderly macro-F1 for ConvNeXt from 0.7528 to 0.8140 and narrows the adult-elderly gap from 0.0917 to 0.0380, with supporting confusion-matrix and saliency-heatmap analyses. The central claim is that simple training modifications can effectively mitigate age-related bias, and that approximate demographic labels are useful for fairness-oriented FER training.

Significance. If the reported elderly improvements were statistically robust and reproducible on other elderly data, this would be a useful contribution: it would show that model-agnostic, architecture-independent training changes can reduce demographic bias without the need for age-conditioned architectural modifications, and that automatically estimated age labels on a large, unlabeled dataset can support bias-aware training. Strengths of the paper include the public code release, the use of three network architectures, the systematic per-expression and per-age-group analysis, the XAI-driven attention comparison, and the comparison against a prior method (Huang et al.) on the same elderly subset. The main weakness is that the entire elderly evidence base is a single, small (114-image) dataset with no surprise class, the reported uncertainties reflect only training-fold variability, and the paper contains no significance testing or correction for multiple comparisons. The consistency claim is also not fully supported by Table 2, since MobileNetV3 with Age-weighted Loss has a lower average F1 than its baseline.

major comments (3)
  1. [Section 4.2, Table 2] The entire elderly evaluation rests on 114 FACES images, with no surprise class, which gives roughly 19 images per expression class. The reported standard deviations (e.g., 0.0182 for the ConvNeXt baseline) are computed over the five training folds on this fixed test set; they do not capture test-set sampling variability. With about 19 samples per class, a small number of prediction changes can shift macro-F1 by several points, so the headline improvement (ConvNeXt baseline 0.7528 to Age-weighted Loss 0.8140) may be within plausible sampling noise. No confidence intervals, bootstrap estimates, or significance tests are reported, and Section 7 concedes that no other elderly dataset was used. To support the abstract's claim that age-related bias is 'effectively mitigated,' the authors should either add a second elderly evaluation set, provide uncertainty estimates that account for test-set sampling, or substantially soften the claim.
  2. [Section 5.2, Figure 6] The 'best approach' for each network is selected on the same test sets used for evaluation, as stated in the caption: 'best-performing approach... based on the average F1 scores reported in Table 2.' This post hoc selection on the test data introduces a multiple-comparisons problem and inflates the apparent success of the chosen method. Moreover, the consistency claim in the abstract ('consistent improvements') is contradicted by Table 2: MobileNetV3 with Age-weighted Loss has an overall average F1 of 0.7740, below its baseline of 0.7804, and its elderly F1 gain is smaller than for the other two networks. The authors should report results for all methods without test-set selection, use a validation split for method choice, or apply multiple-comparison correction when claiming consistency.
  3. [Section 4.2 and Section 6.3] All training-age information comes from MiVOLO's automatic age labels on AffectNet, but the paper provides no quantitative validation of these labels on AffectNet itself. Section 6.3 asserts that the labels were 'sufficiently accurate' and that the results demonstrate this, yet no ground-truth check, error analysis, or sensitivity analysis is reported. Because the age-weighted loss and the age regression task both depend directly on these labels, label noise could bias or attenuate the measured effects of the mitigation strategies. A small manually labeled validation subset, or a perturbation of age-group boundaries, would make the robustness of the conclusions testable.
minor comments (4)
  1. [Table 1] RQ1.2 contains a typo: 'FER??' with a double question mark, which should be corrected.
  2. [Section 6.3] The sentence beginning 'Notably, training on AffectNet allowed our baseline...' reports values 'approximately .7329, .7535 and .7528 accuracy'; these are macro-F1 scores, not accuracy, and should be labeled consistently.
  3. [Sections 5.2 and 6.2] The word 'significant' is used repeatedly (e.g., 'All three strategies significantly reduced this confusion') without any statistical testing; consider replacing it with 'substantial' or reporting appropriate significance measures.
  4. [Figure 2 and Section 4.2] The text 'the image count per expression is approximately: 200+ for children, 500+ for adults, and exactly 114 for elderly' is ambiguous because 114 is the total elderly count, not a per-expression count; please clarify the wording.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: Age-weighted loss is a training reweighting evaluated on held-out FACES, and the central mitigation claim does not reduce to its inputs.

full rationale

The paper's derivation chain is not circular. The Age-weighted Loss (Eq. 1) weights each training sample by N_min / N_{s,a}, the inverse frequency of the (expression, age-group) cell in AffectNet; this is a standard reweighting defined entirely from training-set counts, and the elderly gains are measured on held-out FACES images that were never used to set the weights. No fitted parameter is renamed as a prediction, no target quantity is defined in terms of the method, and no uniqueness theorem is imported from the authors' prior work. The multi-task and multi-modal strategies inject estimated age as a secondary target or input, and their held-out improvements are empirical outcomes rather than identities. Self-citations [19,20,21,43] support the XAI pipeline and background claims, but the bias-mitigation result does not reduce to any of them; the citation about AffectNet generalization is offered as an explanation for baseline superiority, not as the load-bearing step for the mitigation conclusion. Concerns about the 114-image elderly test set (Section 4.2) and unvalidated MiVOLO labels (Section 6.3) are statistical-validity issues, not circularity: even if the result is fragile, it is not equivalent to its inputs by construction.

Assumptions & free parameters 2 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new entities and no fitted constants, but it depends on several unverified domain assumptions. The two most consequential are the sufficiency of automatically estimated age labels and the representativeness of the 114-image FACES elderly set. The unspecified multi-task loss weight and density-weighting bandwidth are unreported hyperparameters that affect the results.

free parameters (2)
  • multi-task loss weight = not reported
    Section 4.5.2 combines age regression MSE with expression cross-entropy using a weighted sum but does not report the weight, which controls how strongly age learning is forced.
  • density-based weighting bandwidth = not reported
    Section 4.5.2 applies density-based weighting for imbalanced regression on the age task; the kernel/bandwidth hyperparameters are unspecified.
assumptions (5)
  • domain assumption MiVOLO automatic age estimates on AffectNet are accurate enough to define age groups and support age-aware training.
    Section 4.2 introduces MiVOLO labels; Section 6.3 asserts they are 'sufficiently accurate' without a ground-truth comparison on AffectNet.
  • domain assumption The FACES elderly subset (114 images, no surprise) is representative of elderly facial expression recognition.
    Section 4.2 selects FACES as the only elderly evaluation dataset; Section 5 uses it for all elderly results.
  • domain assumption Saliency maps faithfully identify the facial regions that drive model predictions.
    Section 4.3 adopts saliency maps for local explanations and uses them to compare attention across age groups and methods.
  • domain assumption Expression labels from AffectNet and from the controlled test datasets denote the same seven categories.
    Section 4.2 merges datasets with different annotation protocols and maps them to a common label set without a consistency check.
  • domain assumption Cross-dataset F1 on controlled frontal-face benchmarks is a valid measure of model generalization and bias.
    All conclusions are based on evaluating AffectNet-trained models on these external datasets.

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Pith. "Pith review of Bridging the gap in FER: addressing age bias in deep learning." pith.science (2026). https://pith.science/paper/NI2GIR2C

@misc{pith2026250707638,
  author       = {Pith},
  title        = {Pith review of: Bridging the gap in FER: addressing age bias in deep learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NI2GIR2C}},
  note         = {Machine review of arXiv:2507.07638}
}
read the original abstract

Facial Expression Recognition (FER) systems based on deep learning have achieved impressive performance in recent years. However, these models often exhibit demographic biases, particularly with respect to age, which can compromise their fairness and reliability. In this work, we present a comprehensive study of age-related bias in deep FER models, with a particular focus on the elderly population. We first investigate whether recognition performance varies across age groups, which expressions are most affected, and whether model attention differs depending on age. Using Explainable AI (XAI) techniques, we identify systematic disparities in expression recognition and attention patterns, especially for "neutral", "sadness", and "anger" in elderly individuals. Based on these findings, we propose and evaluate three bias mitigation strategies: Multi-task Learning, Multi-modal Input, and Age-weighted Loss. Our models are trained on a large-scale dataset, AffectNet, with automatically estimated age labels and validated on balanced benchmark datasets that include underrepresented age groups. Results show consistent improvements in recognition accuracy for elderly individuals, particularly for the most error-prone expressions. Saliency heatmap analysis reveals that models trained with age-aware strategies attend to more relevant facial regions for each age group, helping to explain the observed improvements. These findings suggest that age-related bias in FER can be effectively mitigated using simple training modifications, and that even approximate demographic labels can be valuable for promoting fairness in large-scale affective computing systems.

Figures

Figures reproduced from arXiv: 2507.07638 by the authors.

Figure 1
Figure 1. (a) Class distribution and (b) age histogram of the AffectNet dataset. • Adults (18-59): FEGA [53], RaFD [38], KDEF [9], WSEFEP [50], and FACES [14] • Elderly (60+): FACES [14] While we collected four and five datasets for the children and adult groups, respectively, only one dataset (FACES) contained elderly individuals. Although the Lifespan dataset [46] includes some elderly faces, it was excluded due to class im… view at source ↗
Figure 2
Figure 2. shows the number of images per expression, dataset, and age group. We included the six basic expressions (happiness, sadness, surprise, fear, anger, and disgust), as defined by Ekman [16], plus the neutral expression. However, not all expressions were present in all datasets. For instance, DEFSS and NIMH-ChEFS lack “disgust” and “surprise,” while FACES lacks “surprise.” As seen in the figure, the image count per exp… view at source ↗
Figure 3
Figure 3. We also computed confusion matrices separately for each age group to better understand which expressions were misclassified ( [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (12 more)
Figure 4
Figure 4. Figure 4: Normalized confusion matrices by age group for the baseline approach. From left to right: children, adults, and elderly. From up to bottom: MobileNetV3, Swin Transformer and ConvNeXt-Base. Interestingly, these confusions also appear in the adult group, albeit to a less…
Figure 5
Figure 5. Figure 5: Explanation heatmaps by age group and expression for the ConvNeXt-Base baseline approach. Among the expressions, “happiness” and “disgust” exhibited the most distinct activation patterns. For “happiness”, the model consistently focused its attention on regions such as …
Figure 6
Figure 6. Figure 6: Performance of the best approach based on macro average F1 score by expression and age group. From left to right: MobileNetV3 Multi-modal Input, Swin Transformer Age-weighted Loss and ConvNeXt-Base Age-weighted Loss. “sadness” as “disgust”, resulting in a substantial i…
Figure 7
Figure 7. Figure 7: Normalized confusion matrices by age group for the ConvNeXt-Base Age-weighted Loss approach. From left to right: children, adults, and elderly [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]
Figure 8
Figure 8. Figure 8: Explanation heatmaps by age group and expression for the ConvNeXt-Base Age-weighted Loss approach. 6. Discussion To structure our discussion, we revisit the two research questions outlined in Section 3. 6.1. RQ1: Are there significant differences in model performance a…
Figure 9
Figure 9. Figure 9: Explanation heatmaps by age group and expression for MobileNetV3 baseline approach. A.2. Improvement strategies detailed results [PITH_FULL_IMAGE:figures/full_fig_p015_9.png]
Figure 10
Figure 10. Figure 10: Explanation heatmaps by age group and expression for Swin Transformer baseline approach. confusion between “sadness” and “neutral” expressions. Notably, this particular interaction does not manifest in the adult age group. For the middle-age group, no significant diff…
Figure 11
Figure 11. Figure 11: Per-class performance of the different methods addressing age bias. From left to right: children, adults and elderly. From up to bottom: MobileNetV3, Swin Transformer and ConvNeXt-Base [PITH_FULL_IMAGE:figures/full_fig_p017_11.png]
Figure 12
Figure 12. Figure 12: Normalized confusion matrices by age group for the MobileNetV3 multi-modal approach. From left to right: children, adults and elderly. Gaya-Morey et al.: Preprint submitted to Elsevier Page 17 of 21 [PITH_FULL_IMAGE:figures/full_fig_p017_12.png]
Figure 13
Figure 13. Figure 13: Normalized confusion matrices by age group for the Swin Transformer age-weighted loss approach. From left to right: children, adults and elderly [PITH_FULL_IMAGE:figures/full_fig_p018_13.png]
Figure 14
Figure 14. Figure 14: Explanation heatmaps by age group and expression for MobileNetV3 Multi-modal Input approach. Gaya-Morey et al.: Preprint submitted to Elsevier Page 18 of 21 [PITH_FULL_IMAGE:figures/full_fig_p018_14.png]
Figure 15
Figure 15. Figure 15: Explanation heatmaps by age group and expression for Swin Transformer age-weighted loss approach. Data availability All code is made publicly available at https://github.com/Xavi3398/FER-for-elderly. References [1] Adadi, A., Berrada, M., 2018. Peeking inside the blac…

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.