| Full text | |
| Author(s): |
Marasco, Emanuela
;
Cruz Junior, Luismar B.
Total Authors: 2
|
| Document type: | Journal article |
| Source: | IEEE ACCESS; v. 13, p. 10-pg., 2025-01-01. |
| Abstract | |
Optical sensors, such as the RGB cameras embedded in smartphones, often fail to accurately capture the full spectrum of skin tones. As a result, individuals with darker skin may experience reduced performance in machine vision-based security systems. Insufficient attention to human diversity, including variations in skin tone, can contribute to biased training data and, subsequently, to disparities in AI systems, especially in biometric recognition. This paper highlights the need for more consistent and objective approaches to assessing skin tone, which are often treated subjectively or applied inconsistently. We address this issue by analyzing RGB finger photo data using colorimetric techniques to support the development of more inclusive machine vision systems. (AU) | |
| FAPESP's process: | 13/07276-1 - CEPOF - Optics and Photonic Research Center |
| Grantee: | Vanderlei Salvador Bagnato |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |
| FAPESP's process: | 14/50857-8 - National Institute in Basic Optics and Applied to Life Sciences |
| Grantee: | Vanderlei Salvador Bagnato |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 24/00206-2 - In-vivo photodynamic therapy monitoring and evaluation in non-melanoma skin cancer using spatially modulated images |
| Grantee: | Luismar Barbosa da Cruz Junior |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |