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arxiv: 2604.03335 · v1 · submitted 2026-04-03 · 💻 cs.LG · cs.NE

Apparent Age Estimation: Challenges and Outcomes

Justin Rainier Go , Lorenz Bernard Marqueses , Mikaella Kaye Martinez , John Kevin Patrick Sarmiento , Abien Fred Agarap This is my paper

Pith reviewed 2026-05-13 19:40 UTC · model grok-4.3

classification 💻 cs.LG cs.NE
keywords apparent age estimationdemographic biasmodel fairnesssaliency mapsdistribution learningage estimationFairFace dataset
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The pith

Apparent age estimation models exhibit demographic biases that technical improvements alone cannot fix.

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

The paper reviews existing approaches to apparent age estimation from facial images, focusing on the DEX model enhanced with distribution learning losses such as Mean-Variance Loss and Adaptive Mean-Residue Loss. Evaluations across IMDB-WIKI, APPA-REAL, and FairFace datasets show that AMRL reaches top accuracy levels, yet fairness issues remain, with notable drops in performance for Asian and African American groups. UMAP visualizations display age-based clustering, but saliency maps highlight inconsistent regions of interest depending on the demographic. The authors maintain that without more diverse and localized training data plus rigorous fairness checks, accurate and equitable age estimation will stay out of reach.

Core claim

Despite achieving state-of-the-art accuracy with AMRL, models display significant performance degradation for Asian and African American populations due to inconsistent feature focus revealed in saliency maps. This leads to the conclusion that integration of localized and diverse datasets along with strict fairness validation protocols is essential for fair apparent age estimation.

What carries the argument

Saliency maps that reveal inconsistent feature focus across different demographic groups, alongside UMAP embeddings that show age clustering.

If this is right

  • AMRL improves accuracy over prior methods but maintains precision-equity trade-offs.
  • Performance degradation occurs specifically for Asian and African American populations.
  • Age clustering is evident in embeddings independent of demographic factors.
  • Localized datasets and fairness protocols are necessary beyond algorithmic changes.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • These findings suggest that similar inconsistencies could affect other facial recognition tasks involving diverse populations.
  • Developing region-specific datasets might address generalization issues in real-world deployments.
  • Testing whether data balancing alone resolves the saliency map inconsistencies would clarify the root cause.

Load-bearing premise

The observed performance degradation for Asian and African American populations is due to inconsistent feature focus in saliency maps instead of underlying issues in dataset composition or labels.

What would settle it

Training the models on rebalanced datasets that equalize demographic representation and then verifying if saliency maps show consistent features and performance metrics equalize across groups.

Figures

Figures reproduced from arXiv: 2604.03335 by Abien Fred Agarap, John Kevin Patrick Sarmiento, Justin Rainier Go, Lorenz Bernard Marqueses, Mikaella Kaye Martinez.

Figure 2
Figure 2. Figure 2: APPA-REAL demographic distribution. The Cau￾casian group is highly over-represented [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: IMDB-WIKI and APPA-REAL age distributions [PITH_FULL_IMAGE:figures/full_fig_p002_3.png] view at source ↗
Figure 1
Figure 1. Figure 1: Examples of non-facial images in IMDB-WIKI. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 4
Figure 4. Figure 4: FairFace demographic distribution 2.2 Evaluation Metrics We evaluate models through their mean absolute error (MAE) val￾ues. Additionally, we use the 𝜖-error to account for the degree of uncertainty when estimating the apparent age. Formally defined as 1 − exp  − (𝑥−𝜇) 2 2𝜎 2  , the 𝜖-error fits a normal distribution based on the mean and standard deviation of the collected user guesses for a given image… view at source ↗
Figure 5
Figure 5. Figure 5: Cosine similarity of each image with average em [PITH_FULL_IMAGE:figures/full_fig_p004_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: UMAP-reduced embeddings on APPA-REAL test set [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: UMAP-reduced embeddings using different finetun [PITH_FULL_IMAGE:figures/full_fig_p006_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Saliency maps indicate feature focus inconsistencies [PITH_FULL_IMAGE:figures/full_fig_p006_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Cosine similarity of each image with average em [PITH_FULL_IMAGE:figures/full_fig_p007_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Saliency maps on Filipino celebrities similarly [PITH_FULL_IMAGE:figures/full_fig_p007_10.png] view at source ↗
read the original abstract

Apparent age estimation is a valuable tool for business personalization, yet current models frequently exhibit demographic biases. We review prior works on the DEX method by applying distribution learning techniques such as Mean-Variance Loss (MVL) and Adaptive Mean-Residue Loss (AMRL), and evaluate them in both accuracy and fairness. Using IMDB-WIKI, APPA-REAL, and FairFace, we demonstrate that while AMRL achieves state-of-the-art accuracy, trade-offs between precision and demographic equity persist. Despite clear age clustering in UMAP embeddings, our saliency maps indicate inconsistent feature focus across demographics, leading to significant performance degradation for Asian and African American populations. We argue that technical improvements alone are insufficient; accurate and fair apparent age estimation requires the integration of localized and diverse datasets, and strict adherence to fairness validation protocols.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript reviews the DEX method for apparent age estimation and applies distribution learning techniques such as Mean-Variance Loss (MVL) and Adaptive Mean-Residue Loss (AMRL). It evaluates these on IMDB-WIKI, APPA-REAL, and FairFace datasets, claiming that AMRL achieves state-of-the-art accuracy while trade-offs with demographic equity persist. Saliency maps are presented as evidence of inconsistent feature focus across demographics, which the authors link to performance degradation for Asian and African American populations. The central argument is that technical improvements alone are insufficient, requiring instead localized diverse datasets and strict fairness validation protocols.

Significance. If the empirical findings and causal interpretations hold, the work would usefully highlight limitations of loss-function modifications for mitigating bias in apparent age estimation and reinforce the value of data-centric approaches in fair computer vision. The multi-dataset evaluation and use of saliency maps for qualitative analysis are strengths that could inform future protocol design, though the absence of quantitative saliency metrics or controlled ablations limits the immediate impact on the fairness literature.

major comments (2)
  1. [Results section (saliency maps discussion)] Results section (saliency maps discussion): The assertion that saliency maps reveal inconsistent feature focus that directly causes significant performance degradation for Asian and African American populations is not supported by quantitative validation. No correlation between any focus-inconsistency metric and per-group MAE is reported, nor is there an ablation that holds training data distribution fixed while varying model focus; this leaves open whether dataset imbalances or label noise in IMDB-WIKI and APPA-REAL are the primary drivers.
  2. [Experimental results and abstract] Experimental results and abstract: The claim that AMRL achieves state-of-the-art accuracy is stated without accompanying numerical values, confidence intervals, or comparison tables against the original DEX baseline or other recent methods. This absence prevents verification of the accuracy claim and of the reported trade-offs with demographic equity on FairFace.
minor comments (1)
  1. [Abstract] The abstract would be strengthened by including at least one key quantitative result (e.g., MAE or fairness gap) to make the claims immediately verifiable.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments that highlight opportunities to strengthen the quantitative support and clarity of our claims. We address each major point below and describe the revisions planned for the next version of the manuscript.

read point-by-point responses

  1. Referee: Results section (saliency maps discussion): The assertion that saliency maps reveal inconsistent feature focus that directly causes significant performance degradation for Asian and African American populations is not supported by quantitative validation. No correlation between any focus-inconsistency metric and per-group MAE is reported, nor is there an ablation that holds training data distribution fixed while varying model focus; this leaves open whether dataset imbalances or label noise in IMDB-WIKI and APPA-REAL are the primary drivers.

    Authors: We agree that the current presentation relies primarily on qualitative saliency map inspection paired with observed per-group performance differences. In the revision we will introduce a simple quantitative focus-inconsistency score (standard deviation of normalized saliency entropy across demographic subgroups) and report its correlation with per-group MAE on FairFace. We will also add a short discussion acknowledging that label noise and dataset imbalance in IMDB-WIKI and APPA-REAL remain plausible confounding factors. A full controlled ablation that decouples model focus from training distribution is not feasible within the existing experimental framework without new data-collection or training procedures; we therefore treat this as a limitation rather than a definitive causal demonstration. revision: partial

  2. Referee: Experimental results and abstract: The claim that AMRL achieves state-of-the-art accuracy is stated without accompanying numerical values, confidence intervals, or comparison tables against the original DEX baseline or other recent methods. This absence prevents verification of the accuracy claim and of the reported trade-offs with demographic equity on FairFace.

    Authors: We acknowledge the omission. The revised manuscript will include explicit numerical results: mean absolute error (MAE) values with standard deviations for AMRL, the original DEX baseline, and at least two additional recent methods on IMDB-WIKI, APPA-REAL, and FairFace. A new comparison table will also report per-demographic MAE on FairFace to make the accuracy–equity trade-off directly verifiable. These numbers will be added to both the abstract and the results section. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper conducts an empirical review and evaluation of the DEX method augmented with existing distribution learning losses (MVL and AMRL) on the public datasets IMDB-WIKI, APPA-REAL, and FairFace. No mathematical derivation is presented that reduces a claimed prediction or result to a quantity defined by the authors' own fitted parameters or self-citations. Saliency map observations are used to note inconsistent feature focus but are not part of any closed derivation loop; the recommendation for localized datasets and fairness protocols follows directly from reported performance gaps on external benchmarks rather than from any self-referential construction. The analysis remains self-contained against independent data sources and prior loss functions with no load-bearing self-citation chains or ansatzes imported from the authors' own prior work.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The work rests on standard supervised deep-learning assumptions and the representativeness of three public face datasets; no new free parameters, axioms, or invented entities are introduced.

axioms (1)
  • domain assumption Standard assumptions of supervised learning on image datasets hold for apparent age labels
    The paper treats age labels in IMDB-WIKI, APPA-REAL, and FairFace as reliable ground truth.

pith-pipeline@v0.9.0 · 5449 in / 1218 out tokens · 31474 ms · 2026-05-13T19:40:38.657634+00:00 · methodology

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Reference graph

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