# Is Gender "In-the-Wild" Inference Really a Solved Problem?

arXiv: https://arxiv.org/abs/2105.05794 · HTML: https://mlsanity.com/paper/2105.05794/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 44 references that are in the map.
- First posted on arXiv 12 May 2021.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [RMPE: Regional Multi-person Pose Estimation](https://mlsanity.com/paper/1612.00137/) (2016)
- [CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark](https://mlsanity.com/paper/1812.00324/) (2018)
- [HyperFace: A Deep Multi-task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition](https://mlsanity.com/paper/1603.01249/) (2016)
- [Improving Person Re-identification by Attribute and Identity Learning](https://mlsanity.com/paper/1703.07220/) (2017)
- [HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis](https://mlsanity.com/paper/1709.09930/) (2017)
- [Pose Flow: Efficient Online Pose Tracking](https://mlsanity.com/paper/1802.00977/) (2018)
- [Deep Imbalanced Attribute Classification using Visual Attention Aggregation](https://mlsanity.com/paper/1807.03903/) (2018)
- [Pedestrian Attribute Recognition: A Survey](https://mlsanity.com/paper/1901.07474/) (2019)
- [Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization](https://mlsanity.com/paper/1910.04562/) (2019)
- [A Richly Annotated Dataset for Pedestrian Attribute Recognition](https://mlsanity.com/paper/1603.07054/) (2016)
- [Attribute Recognition by Joint Recurrent Learning of Context and Correlation](https://mlsanity.com/paper/1709.08553/) (2017)
- [Real-time Convolutional Neural Networks for Emotion and Gender Classification](https://mlsanity.com/paper/1710.07557/) (2017)
- [Rethinking of Pedestrian Attribute Recognition: Realistic Datasets with Efficient Method](https://mlsanity.com/paper/2005.11909/) (2020)
- [Age Group and Gender Estimation in the Wild with Deep RoR Architecture](https://mlsanity.com/paper/1710.02985/) (2017)
- [Joint Estimation of Age and Gender from Unconstrained Face Images using Lightweight Multi-task CNN for Mobile Applications](https://mlsanity.com/paper/1806.02023/) (2018)

## Built on by (most cited first, 3 of 3)

- [ASDnB: Merging Face with Body Cues For Robust Active Speaker Detection](https://mlsanity.com/paper/2412.08594/) (2024)
- [YinYang-Net: Complementing Face and Body Information for Wild Gender Recognition](https://mlsanity.com/paper/2107.06847/) (2021)
- [Leveraging Text Guidance for Enhancing Demographic Fairness in Gender Classification](https://mlsanity.com/paper/2512.11015/) (2025)

## Abstract (arXiv)

Soft biometrics analysis is seen as an important research topic, given its relevance to various applications. However, even though it is frequently seen as a solved task, it can still be very hard to perform in wild conditions, under varying image conditions, uncooperative poses, and occlusions. Considering the gender trait as our topic of study, we report an extensive analysis of the feasibility of its inference regarding image (resolution, luminosity, and blurriness) and subject-based features (face and body keypoints confidence). Using three state-of-the-art datasets (PETA, PA-100K, RAP) and five Person Attribute Recognition models, we correlate feature analysis with gender inference accuracy using the Shapley value, enabling us to perceive the importance of each image/subject-based feature. Furthermore, we analyze face-based gender inference and assess the pose effect on it. Our results suggest that: 1) image-based features are more influential for low-quality data; 2) an increase in image quality translates into higher subject-based feature importance; 3) face-based gender inference accuracy correlates with image quality increase; and 4) subjects' frontal pose promotes an implicit attention towards the face. The reported results are seen as a basis for subsequent developments of inference approaches in uncontrolled outdoor environments, which typically correspond to visual surveillance conditions.