Abstract
This paper presents an exhaustive component-based analysis to identify the ethnicity from facial images. The different ethnic groups identified are Asian, African, African American, Asian Middle East, Caucasian and Other. The classification techniques investigated include Decision Trees, Naïve Bayes, Random Forest and K-Nearest Neighbor. Naïve Bayes achieved 84.7 % and 85.6 % accuracy rates for African ethnicity and Asian ethnicity identification, respectively. The Decision Trees achieved 85.8 % for African American ethnicity identification rate, while K-Nearest Neighbor achieved 86.8 % for Asian Middle East ethnicity and Random Forest achieved 90.8 % for Caucasian ethnicity identification rate. This research work achieved an overall ethnicity identification rate of 86.6 %.
Keywords
- Linear Discriminant Analysis
- Facial Image
- Local Binary Pattern
- True Positive Rate
- Machine Learning Algorithm
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Boyseens, A., Viriri, S. (2016). Component-Based Ethnicity Identification from Facial Images. In: Chmielewski, L., Datta, A., Kozera, R., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2016. Lecture Notes in Computer Science(), vol 9972. Springer, Cham. https://doi.org/10.1007/978-3-319-46418-3_26
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DOI: https://doi.org/10.1007/978-3-319-46418-3_26
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