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Toward to Reduction of Bias for Gender and Ethnicity from Face Images using Automated Skin Tone Classification
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Text/Conference Paper
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Datum
2020
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Gesellschaft für Informatik e.V.
Zusammenfassung
This paper proposes and analyzes a new approach for reducing the bias in gender caused
by skin tone from faces based on transfer learning with fine-tuning. The categorization of the ethnicity
was developed based on an objective method instead of a subjective Fitzpatrick scale. A Kmeans
method was used to categorize the color faces using clusters of RGB pixel values. Also, a new
database was collected from the internet and will be available upon request. Our method outperforms
the state of the art and reduces the gender classification bias using the skin-type categorization. The
best results were achieved with VGGNET architecture with 96.71% accuracy and 3.29% error rate.