Classification has emerged as a leading technique for
problem solution and optimization. Classification has been used
extensively in several problems domains. Automated gender
classification is an area of great significance and has great
potential for future research. It offers several industrial
applications in near future such as monitoring, surveillance,
commercial profiling and human computer interaction. Different
methods have been proposed for gender classification like gait,
iris and hand shape. However, majority of techniques for gender
classification are based on facial information. In this paper, a
comparative study of gender classification using different
techniques is presented. The major emphasis of this work is on
the critical evaluation of different techniques used for gender
classification. The comparative evaluation has highlighted major
strengths and limitations of existing gender classification
techniques. Taking an overview of these major problems, our
research is focused on summarizing the literature by highlighting
its strengths and limitations. This study also presents several
areas of future research in the domain of gender classification.