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dc.contributor.authorKhalid Jilani, Shelina*
dc.contributor.authorUgail, Hassan*
dc.contributor.authorBukar, Ali M.*
dc.contributor.authorLogan, Andrew J.*
dc.contributor.authorMunshi, Tasnim*
dc.date.accessioned2018-01-17T16:18:26Z
dc.date.available2018-01-17T16:18:26Z
dc.date.issued2017
dc.identifier.citationKhalid Jilani S, Ugail H, Bukar AM et al (2017) A machine learning approach for ethnic classification: the British Pakistani face. In: Proceedings of the 2017 International Conference on Cyberworlds (CW). 20-22 Sep 2017, University of Chester, Chester, UK: 170-173.en_US
dc.identifier.urihttp://hdl.handle.net/10454/14544
dc.descriptionNoen_US
dc.description.abstractEthnicity 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.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttps://doi.org/10.1109/CW.2017.27en_US
dc.subjectFace; Principal component analysis; Support vector machines; Feature extraction; Classification algorithms; Algorithm design and analysis; Databasesen_US
dc.titleA machine learning approach for ethnic classification: the British Pakistani faceen_US
dc.status.refereedYesen_US
dc.typeConference paperen_US
dc.type.versionNo full-text in the repositoryen_US


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