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Mitigating Bias in Gender, Age and Ethnicity Classification: a Multi-Task Convolution Neural Network Approach

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Academic year: 2021

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Fig. 1. Block diagram of proposed MTCNN for face attribute analysis (F C R = fully connected layer of race classification task, F C A = fully connected layer of age classifi-cation task, F C G = fully connected layer of gender classification task, SM R =
Table 2. The classification accuracy of the race classification considering gender and age instances [%].
Table 3 summarizes the accuracies of age classification. Again, FFNet’s performance indicates for a significant bias, with classification rates ranging from 61.5% for males to 78.7% for Asians
Table 5. The overall mean classification accuracy of race, gender, age and overall attribute classification on BEFA challenge dataset [%].
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