The computer nose best

Shelina Jilani, Hassan Ugail, Andrew Logan

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

1 Citation (Scopus)
183 Downloads (Pure)


The nose is the most central feature on the face which is known to exhibit both gender and ethnic differences. It is a robust feature, invariant to expression and known to contain depth information. In this paper we address the topic of binary ethnicity classificiation from images of the nose, using a novel dataset of South Asian, Pakistani images. To the best of our knowledge, we are one of the first to attempt demographic (ethnicity) based identification based solely on information from the nose.A two-category (Pakistani vs Non-Pakistani) task was used in combination with Deep learning (ResNet) based and VGG-based pre-trained models. A series of experiments were conducted using ResNet-50, ResNet-101, ResNet-152, VGG-Face, VGG-16 and VGG-19, for feature extraction and a Linear Support Vector Machine for classification. The experimental results demonstrate ResNet-50 achieves the highest performance accuracy of 94.1%. In comparison, the highest score for the VGG-based models (VGG-16) was 90.8%. These results demonstrate that information from the nose is sufficient for deep learning models to achieve >90% accuracy on judgements of ethnicity.
Original languageEnglish
Title of host publication2019 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA
Number of pages6
ISBN (Electronic)978-1-7281-2741-5
ISBN (Print)978-1-7281-2742-2
Publication statusPublished - 6 Feb 2020

Publication series

Name2019 13th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2019


  • Computer Vision
  • Face Perception
  • Ethnicity
  • Nose
  • Face features
  • Deep Residual Neural Networks
  • Human Attributes
  • Feature Extraction
  • Pakistani Face Database

ASJC Scopus subject areas

  • Software
  • Information Systems and Management
  • Artificial Intelligence
  • Computer Science Applications


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