Paper
10 June 2014 Gender classification under extended operating conditions
Howard N. Rude, Mateen Rizki
Author Affiliations +
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.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Howard N. Rude and Mateen Rizki "Gender classification under extended operating conditions", Proc. SPIE 9079, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR V, 90790R (10 June 2014); https://doi.org/10.1117/12.2052867
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CITATIONS
Cited by 1 scholarly publication.
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KEYWORDS
Image classification

Video

Image resolution

Classification systems

Feature extraction

Cameras

Binary data

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