Reduction of Bias for Gender and Ethnicity from Face Images using Automated Skin Tone Classification

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 K-means 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.