# Age Group and Gender Estimation in the Wild with Deep RoR Architecture

arXiv: https://arxiv.org/abs/1710.02985 · HTML: https://mlsanity.com/paper/1710.02985/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2017.

## Built on by (most cited first, 11 of 11)

- [FP-Age: Leveraging Face Parsing Attention for Facial Age Estimation in the Wild](https://mlsanity.com/paper/2106.11145/) (2021)
- [From Face to Gait: Weakly-Supervised Learning of Gender Information from Walking Patterns](https://mlsanity.com/paper/2111.00538/) (2021)
- [Is Gender "In-the-Wild" Inference Really a Solved Problem?](https://mlsanity.com/paper/2105.05794/) (2021)
- [Harnessing Unlabeled Data to Improve Generalization of Biometric Gender and Age Classifiers](https://mlsanity.com/paper/2110.04427/) (2021)
- [YinYang-Net: Complementing Face and Body Information for Wild Gender Recognition](https://mlsanity.com/paper/2107.06847/) (2021)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Heterogeneous Face Attribute Estimation](https://mlsanity.com/paper/2403.00561/) (2024)
- [Data Provenance via Differential Auditing](https://mlsanity.com/paper/2209.01538/) (2022)
- [A Hybrid Transformer-Sequencer approach for Age and Gender classification from in-wild facial images](https://mlsanity.com/paper/2403.12483/) (2024)
- [Predicting Gender by First Name Using Character-level Machine Learning](https://mlsanity.com/paper/2106.10156/) (2021)
- [Facial Information Analysis Technology for Gender and Age Estimation](https://mlsanity.com/paper/2111.09303/) (2021)
- [Leveraging Text Guidance for Enhancing Demographic Fairness in Gender Classification](https://mlsanity.com/paper/2512.11015/) (2025)

## Abstract (arXiv)

Automatically predicting age group and gender from face images acquired in unconstrained conditions is an important and challenging task in many real-world applications. Nevertheless, the conventional methods with manually-designed features on in-the-wild benchmarks are unsatisfactory because of incompetency to tackle large variations in unconstrained images. This difficulty is alleviated to some degree through Convolutional Neural Networks (CNN) for its powerful feature representation. In this paper, we propose a new CNN based method for age group and gender estimation leveraging Residual Networks of Residual Networks (RoR), which exhibits better optimization ability for age group and gender classification than other CNN architectures.Moreover, two modest mechanisms based on observation of the characteristics of age group are presented to further improve the performance of age estimation.In order to further improve the performance and alleviate over-fitting problem, RoR model is pre-trained on ImageNet firstly, and then it is fune-tuned on the IMDB-WIKI-101 data set for further learning the features of face images, finally, it is used to fine-tune on Adience data set. Our experiments illustrate the effectiveness of RoR method for age and gender estimation in the wild, where it achieves better performance than other CNN methods. Finally, the RoR-152+IMDB-WIKI-101 with two mechanisms achieves new state-of-the-art results on Adience benchmark.