Race estimation with deep networks
| Autor: | Ridip Dev Choudhury, Kishore Kashyap, Mazida A. Ahmed |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
education.field_of_study
General Computer Science Computer science business.industry Deep learning Population Class activation mapping Globe 020206 networking & telecommunications Racial group 02 engineering and technology Machine learning computer.software_genre medicine.anatomical_structure Robustness (computer science) 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Artificial intelligence education business computer |
| Zdroj: | Journal of King Saud University - Computer and Information Sciences. 34:4579-4591 |
| ISSN: | 1319-1578 |
| DOI: | 10.1016/j.jksuci.2020.11.029 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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