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10 June 2014 Gender classification from video under challenging operating conditions
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Abstract
The literature is abundant with papers on gender classification research. However the majority of such research is based on the assumption that there is enough resolution so that the subject’s face can be resolved. Hence the majority of the research is actually in the face recognition and facial feature area. A gap exists for gender classification under challenging operating conditions—different seasonal conditions, different clothing, etc.—and when the subject’s face cannot be resolved due to lack of resolution. The Seasonal Weather and Gender (SWAG) Database is a novel database that contains subjects walking through a scene under operating conditions that span a calendar year. This paper exploits a subset of that database—the SWAG One dataset—using data mining techniques, traditional classifiers (ex. Naïve Bayes, Support Vector Machine, etc.) and traditional (canny edge detection, etc.) and non-traditional (height/width ratios, etc.) feature extractors to achieve high correct gender classification rates (greater than 85%). Another novelty includes exploiting frame differentials.
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Olga Mendoza-Schrock and Guozhu Dong "Gender classification from video under challenging operating conditions", Proc. SPIE 9079, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR V, 90790S (10 June 2014); https://doi.org/10.1117/12.2052864
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