A machine learning approach for ethnic classification: the British Pakistani face

Shelina Khalid Jilani, Hassan Ugail, Ali M. Bukar, Andrew Logan, Tasnim Munshi

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

12 Citations (Scopus)
152 Downloads (Pure)


Ethnicity is one of the most salient clues to face identity. Analysis of ethnicity-specific facial data is a challenging problem and predominantly carried out using computer-based algorithms. Current published literature focusses on the use of frontal face images. We addressed the challenge of binary (British Pakistani or other ethnicity) ethnicity classification using profile facial images. The proposed framework is based on the extraction of geometric features using 10 anthropometric facial landmarks, within a purpose-built, novel database of 135 multi-ethnic and multi-racial subjects and a total of 675 face images. Image dimensionality was reduced using Principle Component Analysis and Partial Least Square Regression. Classification was performed using Linear Support Vector Machine. The results of this framework are promising with 71.11% ethnic classification accuracy using a PCA algorithm + SVM as a classifier, and 76.03% using PLS algorithm + SVM as a classifier.
Original languageEnglish
Title of host publication2017 International Conference on Cyberworlds (CW)
Number of pages4
ISBN (Electronic)9781538620892
ISBN (Print)9781538620908
Publication statusPublished - 1 Dec 2017


  • machine learning
  • face identity
  • ethnic classification


Dive into the research topics of 'A machine learning approach for ethnic classification: the British Pakistani face'. Together they form a unique fingerprint.

Cite this