Vision-based Human Gender Recognition: A Survey

TL;DR

This survey reviews vision-based human gender recognition methods, highlighting achievements in controlled settings and emphasizing the need for improved robustness in real-world applications.

Contribution

It provides a comprehensive overview of face and body-based gender recognition techniques and discusses challenges and future directions in the field.

Findings

High accuracy in controlled environments

Challenges in real-world robustness

Need for improved generalization methods

Abstract

Gender is an important demographic attribute of people. This paper provides a survey of human gender recognition in computer vision. A review of approaches exploiting information from face and whole body (either from a still image or gait sequence) is presented. We highlight the challenges faced and survey the representative methods of these approaches. Based on the results, good performance have been achieved for datasets captured under controlled environments, but there is still much work that can be done to improve the robustness of gender recognition under real-life environments.