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Proceedings Paper

Gender classification under extended operating conditions
Author(s): Howard N. Rude; Mateen Rizki
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Paper Abstract

Gender classification is a critical component of a robust image security system. Many techniques exist to perform gender classification using facial features. In contrast, this paper explores gender classification using body features extracted from clothed subjects. Several of the most effective types of features for gender classification identified in literature were implemented and applied to the newly developed Seasonal Weather And Gender (SWAG) dataset. SWAG contains video clips of approximately 2000 samples of human subjects captured over a period of several months. The subjects are wearing casual business attire and outer garments appropriate for the specific weather conditions observed in the Midwest. The results from a series of experiments are presented that compare the classification accuracy of systems that incorporate various types and combinations of features applied to multiple looks at subjects at different image resolutions to determine a baseline performance for gender classification.

Paper Details

Date Published: 10 June 2014
PDF: 7 pages
Proc. SPIE 9079, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR V, 90790R (10 June 2014); doi: 10.1117/12.2052867
Show Author Affiliations
Howard N. Rude, Wright State Univ. (United States)
Mateen Rizki, Wright State Univ. (United States)


Published in SPIE Proceedings Vol. 9079:
Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR V
Michael A. Kolodny, Editor(s)

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