# Human Gender Classification: A Review

arXiv: https://arxiv.org/abs/1507.05122 · HTML: https://mlsanity.com/paper/1507.05122/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2015.

## Built on by (most cited first, 4 of 4)

- [Deep Generative Views to Mitigate Gender Classification Bias Across Gender-Race Groups](https://mlsanity.com/paper/2208.08382/) (2022)
- [Biased Binary Attribute Classifiers Ignore the Majority Classes](https://mlsanity.com/paper/2403.14435/) (2024)
- [Multi-Task Faces (MTF) Data Set: A Legally and Ethically Compliant Collection of Face Images for Various Classification Tasks](https://mlsanity.com/paper/2311.11882/) (2023)
- [Fairness through Feedback: Addressing Algorithmic Misgendering in Automatic Gender Recognition](https://mlsanity.com/paper/2506.02017/) (2025)

## Abstract (arXiv)

Gender contains a wide range of information regarding to the characteristics difference between male and female. Successful gender recognition is essential and critical for many applications in the commercial domains such as applications of human-computer interaction and computer-aided physiological or psychological analysis. Some have proposed various approaches for automatic gender classification using the features derived from human bodies and/or behaviors. First, this paper introduces the challenge and application for gender classification research. Then, the development and framework of gender classification are described. Besides, we compare these state-of-the-art approaches, including vision-based methods, biological information-based method, and social network information-based method, to provide a comprehensive review in the area of gender classification. In mean time, we highlight the strength and discuss the limitation of each method. Finally, this review also discusses several promising applications for the future work.