# Age and Gender Prediction From Face Images Using Attentional Convolutional Network

arXiv: https://arxiv.org/abs/2010.03791 · HTML: https://mlsanity.com/paper/2010.03791/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2020.

## Built on by (most cited first, 6 of 6)

- [MiVOLO: Multi-input Transformer for Age and Gender Estimation](https://mlsanity.com/paper/2307.04616/) (2023)
- [Toward Explainable AI for Regression Models](https://mlsanity.com/paper/2112.11407/) (2021)
- [Joint Person Identity, Gender and Age Estimation from Hand Images using Deep Multi-Task Representation Learning](https://mlsanity.com/paper/2303.15263/) (2023)
- [Applying the Case Difference Heuristic to Learn Adaptations from Deep Network Features](https://mlsanity.com/paper/2107.07095/) (2021)
- [eXplainable Artificial Intelligence (XAI) in aging clock models](https://mlsanity.com/paper/2307.13704/) (2023)
- [XpertAI: uncovering regression model strategies for sub-manifolds](https://mlsanity.com/paper/2403.07486/) (2024)

## Abstract (arXiv)

Automatic prediction of age and gender from face images has drawn a lot of attention recently, due it is wide applications in various facial analysis problems. However, due to the large intra-class variation of face images (such as variation in lighting, pose, scale, occlusion), the existing models are still behind the desired accuracy level, which is necessary for the use of these models in real-world applications. In this work, we propose a deep learning framework, based on the ensemble of attentional and residual convolutional networks, to predict gender and age group of facial images with high accuracy rate. Using attention mechanism enables our model to focus on the important and informative parts of the face, which can help it to make a more accurate prediction. We train our model in a multi-task learning fashion, and augment the feature embedding of the age classifier, with the predicted gender, and show that doing so can further increase the accuracy of age prediction. Our model is trained on a popular face age and gender dataset, and achieved promising results. Through visualization of the attention maps of the train model, we show that our model has learned to become sensitive to the right regions of the face.