Abstract
Ethnic group classification is a well-researched problem, which has been pursued mainly during the past two decades via traditional approaches of image processing and machine learning. In this paper, we propose a method of classifying an image face into an ethnic group by applying transfer learning from a previously trained classification network for large-scale data recognition. Our proposed method yields state-of-the-art success rates of 99.02%, 99.76%, 99.2%, and 96.7%, respectively, for the four ethnic groups: African, Asian, Caucasian, and Indian.
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Notes
- 1.
LBPH is a combination of local binary pattern (LBP) with the histogram of oriented gradients (HOG) techniques.
- 2.
A periocular region includes the iris, eyes, eyelids, eye lashes, and part of the eyebrows.
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Huri, K., (Omid) David, E., Netanyahu, N.S. (2018). DeepEthnic: Multi-label Ethnic Classification from Face Images. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11141. Springer, Cham. https://doi.org/10.1007/978-3-030-01424-7_59
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