Vision-based Human Gender Recognition: A Survey
It addresses the problem of automated gender recognition for applications like surveillance and human-computer interaction, but is incremental as it only reviews existing methods.
This paper surveys existing computer vision methods for human gender recognition using face and whole-body data, noting that while good performance is achieved in controlled environments, robustness in real-life settings remains a challenge.
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.