# On the effect of age perception biases for real age regression

arXiv: https://arxiv.org/abs/1902.07653 · HTML: https://mlsanity.com/paper/1902.07653/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2019.

## Built on by (most cited first, 5 of 5)

- [Perceptual Score: What Data Modalities Does Your Model Perceive?](https://mlsanity.com/paper/2110.14375/) (2021)
- [Lookism: The overlooked bias in computer vision](https://mlsanity.com/paper/2408.11448/) (2024)
- [When Algorithms Play Favorites: Lookism in the Generation and Perception of Faces](https://mlsanity.com/paper/2506.11025/) (2025)
- [Aesthetics as Structural Harm: Algorithmic Lookism Across Text-to-Image Generation and Classification](https://mlsanity.com/paper/2601.11651/) (2026)
- [Facial Age Estimation for Age Fraud Detection in National ID Systems](https://mlsanity.com/paper/2609.05638/) (2026)

## Abstract (arXiv)

Automatic age estimation from facial images represents an important task in computer vision. This paper analyses the effect of gender, age, ethnic, makeup and expression attributes of faces as sources of bias to improve deep apparent age prediction. Following recent works where it is shown that apparent age labels benefit real age estimation, rather than direct real to real age regression, our main contribution is the integration, in an end-to-end architecture, of face attributes for apparent age prediction with an additional loss for real age regression. Experimental results on the APPA-REAL dataset indicate the proposed network successfully take advantage of the adopted attributes to improve both apparent and real age estimation. Our model outperformed a state-of-the-art architecture proposed to separately address apparent and real age regression. Finally, we present preliminary results and discussion of a proof of concept application using the proposed model to regress the apparent age of an individual based on the gender of an external observer.