Mitigating bias in gender, age and ethnicity classification: A multi-task convolution neural network approach

Abstract

This work explores joint classification of gender, age and race. Specifically, we here propose a Multi-Task Convolution Neural Network (MTCNN) employing joint dynamic loss weight adjustment towards classification of named soft biometrics, as well as towards mitigation of soft biometrics related bias. The proposed algorithm achieves promising results on the UTKFace and the Bias Estimation in Face Analytics (BEFA) datasets and was ranked first in the BEFA Challenge of the European Conference of Computer Vision (ECCV) 2018.