  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.
