On the ethnic classification of Pakistani face using deep learning

Shelina Khalid Jilani*, Hassan Ugail, Ali Maina Bukar, Andrew Logan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)
296 Downloads (Pure)


Demographic-based identification plays an active role in the field of face identification. Over the past decade, machine learning algorithms have been used to investigate challenges surrouding ethnic classification for specific populations, such as African, Asian and Caucasian people. Ethnic classification for individuals of South Asian, Pakistani heritage, however, remains to be addressed. The present paper addresses a two-category (Pakistani Vs Non-Pakistani) classification task from a novel, purpose-built dataset. To the best of our knowledge, this work is the first to report a machine learning ethnic classification task with South Asian (Pakistani) faces. We conduted a series of experiments using deep learning algorithms (ResNet-50, ResNet-101 and ResNet-152) for feature extraction and a linear support vector machine (SVM) for classification. The experimental results demonstrate ResNet-101 achieves the highest performance accuracy of 99.2% for full-face ethnicity classification, followed closely by 91.7% and 95.7% for the nose and mouth respectively.
Original languageEnglish
Title of host publication2019 International Conference on Cyberworlds (CW)
Number of pages8
ISBN (Electronic)9781728122977
ISBN (Print)9781728122984
Publication statusPublished - 5 Dec 2019

Publication series

ISSN (Print)2642-357X
ISSN (Electronic)2642-3596


  • ethnicity
  • Pakistani
  • deep learning
  • residual network
  • classification


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