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Classifier-based analysis of visual inspection: Gender differences in decision-making

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5 Author(s)
Heidl, W. ; Machine Vision Dept., Profactor GmbH, Steyr-Gleink, Austria ; Thumfart, S. ; Eitzinger, C. ; Lughofer, E.
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Among manufacturing companies there is a wide-spread consensus that women are better suited to perform visual quality inspection, having higher endurance and making decisions with better reproducibility. Up to now gender-differences in visual inspection decision making have not been thoroughly investigated. We propose a machine learning approach to model male and female decisions with classifiers and base the analysis of gender-differences on the identified model parameters. A study with 50 male and 50 female subjects on a visual inspection task of stylized die-cast parts revealed significant gender-differences in the miss rate (p = 0.002), while differences in overall accuracy are not significant (p = 0.34). On a more detailed level, the application of classifier models shows gender differences are most prominent in the judgment of scratch lengths (p = 0.005). Our results suggest, that gender-differences in visual inspection are significant and that classifier-based modeling is a promising approach for analysis of these tasks.

Published in:

Systems Man and Cybernetics (SMC), 2010 IEEE International Conference on

Date of Conference:

10-13 Oct. 2010

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