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Measuring the Gender and Ethnicity Bias in Deep Models for Face Recognition

  • Alejandro AcienEmail author
  • Aythami MoralesEmail author
  • Ruben Vera-RodriguezEmail author
  • Ivan BartolomeEmail author
  • Julian FierrezEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)

Abstract

We explore the importance of gender and ethnic attributes in the decision-making of face recognition technologies. Our work is in part motivated by the new European regulation for personal data protection, which forces data controllers to avoid discriminative hazards while managing sensitive data like biometric data. The experiments in this paper are aimed to study what extent sensitive data like gender or ethnic origin attributes are present in the most common face recognition networks. For this, our experiments include two popular pre-trained networks: VGGFace and Resnet50. Both pre-trained models are able to classify gender and ethnicity easily (over 95% of performance) even suppressing 80% of the neurons in their embedding layers. The experimentation is conducted on a publicly available database known as Labeled Faces in the Wild with more than 13000 images of faces with a huge range of poses, ages, races and nationalities.

Keywords

Face recognition Human attributes Gender Ethnic Discrimination 

Notes

Acknowledgments

This work was funded by the project CogniMetrics (TEC2015-70627-R) and Bio-Guard (Ayudas Fundación BBVA a Equipos de Investigación Científica 2017).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Biometrics and Data Pattern Analytics (BiDA) Lab, EPSUniversidad Autonoma de MadridMadridSpain

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