Open Access

ARTICLE

Race Classification Using Deep Learning

Khalil Khan¹, Rehan Ullah Khan², Jehad Ali³, Irfan Uddin⁴, Sahib Khan⁵, Byeong-hee Roh^3,*

1 Department of Information Technology and Computer Science, Pak-Austria Institute of Applied Science and Technology, Haripur, Pakistan
2 Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia
3 Department of Computer Engineering and Department of AI Convergence Network, Ajou University, Suwon, Korea
4 Department of Computer Science, Superior College, Lahore, Pakistan
5 Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy

* Corresponding Author: Byeong-hee Roh. Email:

Computers, Materials & Continua 2021, 68(3), 3483-3498. https://doi.org/10.32604/cmc.2021.016535

Received 04 January 2021; Accepted 08 March 2021; Issue published 06 May 2021

Abstract

Race classification is a long-standing challenge in the field of face image analysis. The investigation of salient facial features is an important task to avoid processing all face parts. Face segmentation strongly benefits several face analysis tasks, including ethnicity and race classification. We propose a race-classification algorithm using a prior face segmentation framework. A deep convolutional neural network (DCNN) was used to construct a face segmentation model. For training the DCNN, we label face images according to seven different classes, that is, nose, skin, hair, eyes, brows, back, and mouth. The DCNN model developed in the first phase was used to create segmentation results. The probabilistic classification method is used, and probability maps (PMs) are created for each semantic class. We investigated five salient facial features from among seven that help in race classification. Features are extracted from the PMs of five classes, and a new model is trained based on the DCNN. We assessed the performance of the proposed race classification method on four standard face datasets, reporting superior results compared with previous studies.

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Citations