Mapeamento Sistemático da Literatura sobre Detecção de Pessoas Negras em Obras Audiovisuais com Visão Computacional
Resumo
Este trabalho apresenta um Mapeamento Sistemático da Literatura sobre o uso de Visão Computacional na análise da representatividade de pessoas negras em produções audiovisuais. O objetivo é identificar quais metodologias predominam na detecção de pessoas negras e as lacunas persistentes na área. A pesquisa seguiu a metodologia PRISMA, resultando em 23 estudos selecionados no período de 2020 a 2025. Os resultados indicam que, embora modelos como YOLO, ResNet e VGG apresentem alta acurácia no geral, há desigualdades raciais nos desempenhos dos modelos. Além disso, há uma baixa preocupação ética com a curadoria de dados e ainda são escassas as abordagens que consideram a diversidade fenotípica e cultural.
Palavras-chave:
Ciência de Dados, Inteligência Artificial, Visão Computacional
Referências
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Bamman, D., Samberg, R., So, R. J., and Zhou, N. (2024). Measuring diversity in hollywood through the large-scale computational analysis of film. Proceedings of the National Academy of Sciences, 121(46):e2409770121.
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Bouraya, S. and Belangour, A. (2021). Approaches to video real time multi-object tracking and object detection: A survey. In 2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA), pages 145–151.
Buolamwini, J. and Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. In Friedler, S. A. and Wilson, C., editors, Proceedings of the 1st Conference on Fairness, Accountability and Transparency, volume 81 of Proceedings of Machine Learning Research, pages 77–91. PMLR.
Hirota, Y., Nakashima, Y., and Garcia, N. (2022). Gender and racial bias in visual question answering datasets. In ACM Conference on Fairness, Accountability, and Transparency, New York, NY, USA. Association for Computing Machinery.
Hooks, B. (2023). Cinema vivido: raça, classe e gênero no cinema. Editora Elefante, São Paulo, Brasil, 1 edition. Tradução de Stephanie Borges. Obra original: Reel to Real: Race, Sex, and Class at the Movies, 1996.
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Irawan, E., Mantoro, T., Ayu, M. A., and Asian, J. (2020). Multi-faces recognition in crowd using support vector machine on histogram of gradient. In 2020 6th International Conference on Computing Engineering and Design (ICCED), pages 1–4.
Kagan, D., Levy, M., Fire, M., and Alpert, G. F. (2022). Ethnic representation analysis of commercial movie posters. DOI: 10.1145/1457199.1457235
Kancharlapalli, T. P. and Dwivedi, P. (2023). A novel approach for age and gender detection using deep convolution neural network. In 2023 10th International Conference on Computing for Sustainable Global Development (INDIACom), pages 873–878.
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Krishnan, A., Neas, B., and Rattani, A. (2022). Is facial recognition biased at nearinfrared spectrum as well? In 2022 IEEE International Symposium on Technologies for Homeland Security (HST), pages 1–7.
Kumar, D., Kumar, R. K., Garain, J., Kisku, D. R., Sing, J. K., and Gupta, P. (2024). Dem-ufr: Deep ensemble method for enhanced unconstraint face recognition system. In 2024 OITS International Conference on Information Technology (OCIT).
Luccioni, A. S., Akiki, C., Mitchell, M., and Jernite, Y. (2023). Stable bias: Analyzing societal representations in diffusion models.
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Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., McGuinness, L. A., Stewart, L. A., Thomas, J., Tricco, A. C., Welch, V. A., Whiting, P., and Moher, D. (2021). The prisma 2020 statement: an updated guideline for reporting systematic reviews. BMJ, page n71.
Quijano, A. (2005). Colonialidade do poder, eurocentrismo e américa latina. In A colonialidade do saber: eurocentrismo e ciências sociais. Perspectivas latino-americanas. CLACSO – Consejo Latinoamericano de Ciencias Sociales, Buenos Aires, Argentina.
Rodrigues, A. M. d. S. (2025). É uma questão de dados? o ponto de vista do negro em relação a discriminação algorítmica. Dissertação de mestrado, Pontifícia Universidade Católica de São Paulo (PUC SP), São Paulo, Brasil.
Scheuerman, M. K., Hanna, A., and Denton, R. (2021). Do datasets have politics? disciplinary values in computer vision dataset development. Proc. ACM Hum.-Comput. Interact., 5(CSCW2).
Silva, F. and Silva, T. (2024). Teoria Crítica da Raça na Regulação de Inteligência Artificial.
Silva, T. (2020). Visão computacional e vieses racializados: Branquitude como padrão no aprendizado de máquina. Revista da ABPN, 12(31):428–448. Licenciado sob uma Licença Creative Commons.
Steed, R. and Caliskan, A. (2021). Image representations learned with unsupervised pre-training contain human-like biases. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, FAccT ’21, page 701–713. ACM.
Wang, S. (2023). Design of real-time object recognition algorithm based on deep convolution neural network. In 2023 International Conference on Internet of Things, Robotics and Distributed Computing (ICIRDC), pages 533–538.
Xu, C. (2023). Harmonyvisage: Ethical facial emotion dataset using advanced generative ai. In 2023 IEEE 14th Annual Ubiquitous Computing, Electronics Mobile Communication Conference (UEMCON), pages 0558–0563.
Yucer, S., Tektas, F., Moubayed, N. A., and Breckon, T. P. (2023). Racial bias within face recognition: A survey.
Bamman, D., Samberg, R., So, R. J., and Zhou, N. (2024). Measuring diversity in hollywood through the large-scale computational analysis of film. Proceedings of the National Academy of Sciences, 121(46):e2409770121.
Bento, M. A. S. (2017). Branqueamento e branquitude no brasil. In Carone, I. and Bento, M. A. S., editors, Psicologia social do racismo: estudos sobre branquitude e branqueamento no Brasil, page 645. Editora Vozes, Petrópolis, RJ, Brasil.
Bhattacharjee, S. and Ribes, D. (2025). Technical responses to critique: The case of skin tone. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, CHI ’25, New York, NY, USA. Association for Computing Machinery.
Bouraya, S. and Belangour, A. (2021). Approaches to video real time multi-object tracking and object detection: A survey. In 2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA), pages 145–151.
Buolamwini, J. and Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. In Friedler, S. A. and Wilson, C., editors, Proceedings of the 1st Conference on Fairness, Accountability and Transparency, volume 81 of Proceedings of Machine Learning Research, pages 77–91. PMLR.
Hirota, Y., Nakashima, Y., and Garcia, N. (2022). Gender and racial bias in visual question answering datasets. In ACM Conference on Fairness, Accountability, and Transparency, New York, NY, USA. Association for Computing Machinery.
Hooks, B. (2023). Cinema vivido: raça, classe e gênero no cinema. Editora Elefante, São Paulo, Brasil, 1 edition. Tradução de Stephanie Borges. Obra original: Reel to Real: Race, Sex, and Class at the Movies, 1996.
Hoque, M. N., Saquib, N., Billah, S. M., and Mueller, K. (2020). Toward interactively balancing the screen time of actors based on observable phenotypic traits in live telecast. Proc. ACM Hum.-Comput. Interact., 4(CSCW2).
Irawan, E., Mantoro, T., Ayu, M. A., and Asian, J. (2020). Multi-faces recognition in crowd using support vector machine on histogram of gradient. In 2020 6th International Conference on Computing Engineering and Design (ICCED), pages 1–4.
Kagan, D., Levy, M., Fire, M., and Alpert, G. F. (2022). Ethnic representation analysis of commercial movie posters. DOI: 10.1145/1457199.1457235
Kancharlapalli, T. P. and Dwivedi, P. (2023). A novel approach for age and gender detection using deep convolution neural network. In 2023 10th International Conference on Computing for Sustainable Global Development (INDIACom), pages 873–878.
Khan, Z. and Fu, Y. (2021). One label, one billion faces: Usage and consistency of racial categories in computer vision. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, FAccT ’21, page 587–597. ACM.
Krishnan, A., Neas, B., and Rattani, A. (2022). Is facial recognition biased at nearinfrared spectrum as well? In 2022 IEEE International Symposium on Technologies for Homeland Security (HST), pages 1–7.
Kumar, D., Kumar, R. K., Garain, J., Kisku, D. R., Sing, J. K., and Gupta, P. (2024). Dem-ufr: Deep ensemble method for enhanced unconstraint face recognition system. In 2024 OITS International Conference on Information Technology (OCIT).
Luccioni, A. S., Akiki, C., Mitchell, M., and Jernite, Y. (2023). Stable bias: Analyzing societal representations in diffusion models.
Malik, M., Hopp, F., and Weber, R. (2022). Representations of racial minorities in popular movies: A content-analytic synergy of computer vision and network science. Computational Communication Research, 4(1):208–253. Publisher Copyright: © Musa Malik, Frederic R. Hopp & René Weber.
Mikaeel, V., Turan, B., and Abdulrazaq, M. (2024). Facial race and gender recognition based on convolutional neural network models. Gaziosmanpaşa Bilimsel Araştırma Dergisi, 13(3):1–18.
Mohammed, A.-Q. and Atay, M. (2025). Evaluating the impact of gender on facial expression recognition. In SoutheastCon 2025, pages 906–911.
Neff, K. L., Smith, S. L., and Pierper, K. (2025). Inequality in 1,800 popular films: Examining portrayals of gender, race/ethnicity, lgbtq+ characters with disabilities from 2007 to 2024. In Annenberg Inclusion Initiative.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., McGuinness, L. A., Stewart, L. A., Thomas, J., Tricco, A. C., Welch, V. A., Whiting, P., and Moher, D. (2021). The prisma 2020 statement: an updated guideline for reporting systematic reviews. BMJ, page n71.
Quijano, A. (2005). Colonialidade do poder, eurocentrismo e américa latina. In A colonialidade do saber: eurocentrismo e ciências sociais. Perspectivas latino-americanas. CLACSO – Consejo Latinoamericano de Ciencias Sociales, Buenos Aires, Argentina.
Rodrigues, A. M. d. S. (2025). É uma questão de dados? o ponto de vista do negro em relação a discriminação algorítmica. Dissertação de mestrado, Pontifícia Universidade Católica de São Paulo (PUC SP), São Paulo, Brasil.
Scheuerman, M. K., Hanna, A., and Denton, R. (2021). Do datasets have politics? disciplinary values in computer vision dataset development. Proc. ACM Hum.-Comput. Interact., 5(CSCW2).
Silva, F. and Silva, T. (2024). Teoria Crítica da Raça na Regulação de Inteligência Artificial.
Silva, T. (2020). Visão computacional e vieses racializados: Branquitude como padrão no aprendizado de máquina. Revista da ABPN, 12(31):428–448. Licenciado sob uma Licença Creative Commons.
Steed, R. and Caliskan, A. (2021). Image representations learned with unsupervised pre-training contain human-like biases. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, FAccT ’21, page 701–713. ACM.
Wang, S. (2023). Design of real-time object recognition algorithm based on deep convolution neural network. In 2023 International Conference on Internet of Things, Robotics and Distributed Computing (ICIRDC), pages 533–538.
Xu, C. (2023). Harmonyvisage: Ethical facial emotion dataset using advanced generative ai. In 2023 IEEE 14th Annual Ubiquitous Computing, Electronics Mobile Communication Conference (UEMCON), pages 0558–0563.
Yucer, S., Tektas, F., Moubayed, N. A., and Breckon, T. P. (2023). Racial bias within face recognition: A survey.
Publicado
04/12/2025
Como Citar
ÁVILA, Anne G. M.; MACHADO, Gabriel M. O.; DO NASCIMENTO, Maria de Jesus; SILVEIRA, Raquel; DE OLIVEIRA, Carina Teixeira.
Mapeamento Sistemático da Literatura sobre Detecção de Pessoas Negras em Obras Audiovisuais com Visão Computacional . In: ESCOLA REGIONAL DE COMPUTAÇÃO DO CEARÁ, MARANHÃO E PIAUÍ (ERCEMAPI), 13. , 2025, São Luís/MA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 11-20.
DOI: https://doi.org/10.5753/ercemapi.2025.17495.
