Date of Graduation


Document Type


Degree Type



Statler College of Engineering and Mineral Resources


Lane Department of Computer Science and Electrical Engineering

Committee Chair

Guodong Guo

Committee Co-Chair

Arun A Ross

Committee Member

Cun-Quan Zhang


Gender recognition has many useful applications, ranging from business intelligence, through to image search and social activity analysis. Traditional research on gender recognition focuses on face images in a constrained environment. In this work, we propose a novel problem of gender recognition in articulated human body images acquired from an unconstrained environment in the real world. Our empirical study answers the question of whether gender recognition can be performed in articulated body images, and discovers important issues such as which body parts are informative, how many body parts are needed to combine together, and what representations are good for articulated body-based gender recognition. We also pursue data fusion schemes and efficient feature dimensionality reduction based on the partial least squares estimation. Extensive experiments are performed on two unconstrained databases that have not been explored before for gender recognition. At the end of this work, we also present some preliminary results on the automatic description of the appearance of the upper body.