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The LFW-Gender Dataset

  • Ahsan Jalal
  • Usman Tariq
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10118)

Abstract

Gender identification is a precursor for context specific emotion recognition. Small but significant differences have been noted across different gender groups in terms of emotion expressiveness. Apart from facial expressions and security applications, gender recognition is becoming increasingly relevant after the rise of applications involving social media platforms. Labelled Faces in the Wild (LFW) dataset is designed for studying the problem of face recognition under unconstrained environment. However, it is used to study other facial attributes as well, including gender. In this paper, we propose a standardized subset of LFW database (LFW-gender) that can be used as a benchmark for gender recognition algorithms. We also provide a baseline for performance on the dataset for gender recognition with various algorithms and some results may suggest that this is a harder subset to classify.

Keywords

Support Vector Machine Face Image Independent Component Analysis Emotion Expressiveness Random Projection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work was supported in part by a faculty research grant, FRG15-R-42. We thank the efforts of Mohamed, Riaz, Siyam and Zeid who helped in manual verification of gender labels.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Electrical Engineering, College of EngineeringAmerican University of SharjahSharjahUAE

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