期刊名称：Journal of King Saud University @?C Computer and Information Sciences
印刷版ISSN：1319-1578
出版年度：2022
卷号：34
期号：7
页码：4579-4591
语种：English
出版社：Elsevier
摘要：Identifying race, which is a major physical feature in humans, is still a challenging task owing much to the lack of a concrete definition of race and the diversity of population across the globe. In this paper, we try to address the problem of race identification of four broad racial groups namely Caucasian, African, Asian and Indian. The newly developed BUPT Equalised Face dataset bearing about 1.3 M images in unrestricted environment is used to train our deep convolution network (R-Net) which achieves a state-of-the-art accuracy of 97%. To test the validity of this model it is evaluated on other datasets namely UTK and CFD. R-Net is also compared with fine-tuned VGG16 model for race estimation. Experimental results prove the robustness of this model for use in unconstrained environments. And finally, Gradient-weighted Class Activation Mapping (Grad-CAM) is applied to get a visual explanation of the deep learning model.
