Statler College of Engineering and Mining Resources
Lane Department of Computer Science and Electrical Engineering
Gender recognition has many useful applications, ranging from business intelligence to image search and social activity analysis. Traditional research on gender recognition focuses on face images in a constrained environment. This paper proposes a method for gender recognition in articulated human body images acquired from an unconstrained environment in the real world. A systematic study of some critical issues in body-based gender recognition, 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, is also presented. This paper also pursues data fusion schemes and efficient feature dimensionality reduction based on the partial least squares estimation. Extensive experiments are performed on two unconstrained databases which have not been explored before for gender recognition.
Digital Commons Citation
Wu, Qin and Guo, Guodong, "Gender Recognition from Unconstrained and Articulated Human Body" (2014). Faculty & Staff Scholarship. 2594.
Wu, Q., & Guo, G. (2014). Gender Recognition from Unconstrained and Articulated Human Body. The Scientific World Journal, 2014, 1–12. https://doi.org/10.1155/2014/513240