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Symmetry 2018, 10(7), 232; https://doi.org/10.3390/sym10070232

Facial Asymmetry-Based Anthropometric Differences between Gender and Ethnicity

1
Department of Electrical Engineering, Mirpur University of Science and Technology, Mirpur 10250 (AJK), Pakistan
2
Faculty of Computing, Engineering and Science, Staffordshire University, Stoke-on-Trent ST4 2DE, UK
3
Department of Electrical (Power) Engineering, Mirpur University of Science and Technology, Mirpur 10250 (AJK), Pakistan
4
School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Received: 10 May 2018 / Revised: 18 June 2018 / Accepted: 18 June 2018 / Published: 21 June 2018
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Abstract

Bilateral facial asymmetry is frequently exhibited by humans but its combined evaluation across demographic traits including gender and ethnicity is still an open research problem. In this study we measure and evaluate facial asymmetry across gender and different ethnic groups and investigate the differences in asymmetric facial dimensions among the subjects from two public face datasets, the MORPH and FERET. To this end, we detect 28 facial asymmetric dimensions from each face image using an anthropometric technique. An exploratory analysis is then performed via a multiple linear regression model to determine the impact of gender and ethnicity on facial asymmetry. Post-hoc Tukey test has been used to validate the results of the proposed method. The results show that out of 28 asymmetric dimensions, females differ in 25 dimensions from males. African, Asian, Hispanic and other ethnic groups have asymmetric dimensions that differ significantly from those of Europeans. These findings could be important to certain applications like the design of facial fits, as well as guidelines for facial cosmetic surgeons. Lastly, we train a neural network classifier that employs asymmetric dimensions for gender and race classification. The experimental results show that our trained classifier outperforms the support vector machine (SVM) and k-nearest neighbors (kNN) classifiers. View Full-Text
Keywords: facial asymmetry; gender; ethnicity; datasets; linear regression; classification facial asymmetry; gender; ethnicity; datasets; linear regression; classification
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Sajid, M.; Shafique, T.; Riaz, I.; Imran, M.; Jabbar Aziz Baig, M.; Baig, S.; Manzoor, S. Facial Asymmetry-Based Anthropometric Differences between Gender and Ethnicity. Symmetry 2018, 10, 232.

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