Towards a fairer vision: addressing class imbalance in image gender recognition
This paper addresses the growing integration of machine learning and computer vision with a particular emphasis on classifying facial features. Although these technologies hold great potential, there are concerns regarding biases that must be mitigated to ensure more impartial results. This study pr...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universidad Autónoma de Madrid |
| Repositorio: | Biblos-e Archivo. Repositorio Institucional de la UAM |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.uam.es:10486/728980 |
| Acceso en línea: | https://hdl.handle.net/10486/728980 https://dx.doi.org/10.1007/s00138-025-01732-6 |
| Access Level: | acceso abierto |
| Palabra clave: | Bias Mitigation Class Imbalance Human Data Informática |
| Sumario: | This paper addresses the growing integration of machine learning and computer vision with a particular emphasis on classifying facial features. Although these technologies hold great potential, there are concerns regarding biases that must be mitigated to ensure more impartial results. This study primarily addresses the issue of bias caused by class imbalance, which can be mitigated through algorithmic or data-level approaches. Notably, the literature presents gaps, including the lack of comprehensive studies comparing these two types of mitigation techniques and understanding the circumstances in which each is more suitable. Moreover, the influence of imbalance conditions and data complexity on mitigation meth¬ods remains underexplored. To address these gaps, this research formulates three key research questions and conducts experiments using two datasets, UTKFace and PlantVillage, known for varying complexities. Various imbalance scenarios are simulated in these datasets. Additionally, a novel algorithm-level mitigation method named “Diffuse Focal Loss” is introduced. Results indicate the high effectiveness of synthetic oversampling methods, specifically using the Wasserstein variant of Generative Adversarial Networks, compared to algorithm-level approaches. Among the latter, the proposed novel method outperforms others in terms of metrics. However, it is worth noting that algorithmic techniques are more practical and quicker to apply in low-complexity scenarios |
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