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Apparent Age Estimation: Challenges and Outcomes

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

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.

Apparent Age Estimation: Challenges and Outcomes

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.

Paper Structure

This paper contains 27 sections, 10 figures, 5 tables.

Table of Contents

  1. Introduction and Related Works
  2. DEX
  3. Mean-Variance Loss
  4. Adaptive Mean-Residue Loss
  5. Contributions
  6. Exploring Age Estimation datasets
  7. Datasets Description
  8. IMDB-WIKIpedia
  9. ChaLearn Looking at People (CLAP)
  10. APPA-REAL
  11. FairFace
  12. Evaluation Metrics
  13. Modelling
  14. Bias and fairness assessment
  15. Software/Hardware configuration
  16. ...and 12 more sections

Figures (10)

  • Figure 1: Examples of non-facial images in IMDB-WIKI. Note the presence of pixel-stretching artifacts.
  • Figure 2: APPA-REAL demographic distribution. The Caucasian group is highly over-represented.
  • Figure 3: IMDB-WIKI and APPA-REAL age distributions
  • Figure 4: FairFace demographic distribution
  • Figure 5: Cosine similarity of each image with average embeddings for age, grouped by race
  • ...and 5 more figures