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

  • Abhijit DasEmail author
  • Antitza Dantcheva
  • Francois Bremond
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11129)

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.

Keywords

Bias Facial analysis Age Gender and race Soft biometrics Facial attributes 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Abhijit Das
    • 1
    Email author
  • Antitza Dantcheva
    • 1
  • Francois Bremond
    • 1
  1. 1.InriaSophia AntipolisFrance

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