Gender Recognition
Introduction
Being able to detect with a high degree of accuracy the gender of an individual has many applications (particularly in the
world of advertising) and developing an effective technique to determine the gender of an individual from a live camera
feed is our goal for this project.Once completed, our method would be able to determine the gender of individuals in front
of a camera either within crowds or when by themselves. Thus, it would be possible to assess whether there are more women
or men present in a given camera viewing range.We are currently comparing various state of the art feature extraction methods
that could be both applied and built upon in order to determine the gender of human subjects. The facial feature extraction
methods we are currently experimenting with are: Uniform Local Binary Pattern, Local Binary Pattern and Brief. Then, once an accurate feature extraction method is found machine
learning will be used: image databases with appropriate ground truth are used to train a system to recognize the ways in which
males and females differ.Once a viable method is found, it will be used in conjunction with the face detection API that we had
developed in the related Face Recognition project in order to use a camera feed instead of static images. Currently, the focus
is on using an individual's facial features to determine their gender, but there is also potential in using other features, such
as a person's hair length, as well as how much skin is visible in their upper body and shoulders.In terms of real world applicability
of this project, gender detection of people from a live camera feed can be used to display targeted advertisement to individuals
viewing a screen. Such tailored advertising would allow an ad to be more effective in reaching the viewers, as well as, allow the
content to be made more engaging to those belonging to the detected gender demographic's majority.