@InProceedings{MenezesFerrPereGome:2021:BiFaFa,
               author = "Menezes, Hanna Fran{\c{c}}a and Ferreira, Arthur Silva Cavalcante 
                         and Pereira, Eanes Torres and Gomes, Herman Martins",
          affiliation = "{Universidade Federal de Campina Grande } and {Universidade 
                         Federal de Campina Grande } and {Universidade Federal de Campina 
                         Grande } and {Universidade Federal de Campina Grande}",
                title = "Bias and Fairness in Face Detection",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Bias, Fairness, Face Detection.",
             abstract = "Processing of face images is used in many areas, for example: 
                         commercial applications such as video-games; facial biometrics; 
                         facial expression recognition, etc. Face detection is a crucial 
                         step for any system that processes face images. Therefore, if 
                         there is bias or unfairness in this first step, all the processing 
                         steps that follow may be compromised. Errors in automatic face 
                         detection may be harmful to people as, for instance, in situations 
                         where a decision may limit or restrict their freedom to come and 
                         go. Therefore, it is crucial to investigate the existence of these 
                         errors caused due to bias or unfairness. In this paper, an 
                         analysis of five well-known top accuracy face detectors is 
                         performed to investigate the presence of bias and unfairness in 
                         their results. Some of the metrics used to identify the existence 
                         of bias and unfairness involved the verification of demographic 
                         parity, verification of existence of false positives and/or false 
                         negatives, rate of positive prediction, and verification of 
                         equalized odds. Data from about 365 different individuals were 
                         randomly selected from the Facebook Casual Conversations Dataset, 
                         resulting in approximately 5,500 videos, providing 550,000 frames 
                         used for face detection in the performed experiments. The obtained 
                         results show that all five face detectors presented a high risk of 
                         not detecting faces from the female gender and from people between 
                         46 and 85 years old. Furthermore, the skin tone groups related 
                         with dark skin are the groups pointed out with highest risk of 
                         faces not being detected for four of the five evaluated face 
                         detectors. This paper points out the necessity of the research 
                         community to engage in breaking the perpetuation of injustice that 
                         may be present in datasets or machine learning models.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
                  doi = "10.1109/SIBGRAPI54419.2021.00041",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI54419.2021.00041",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45CKFQB",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45CKFQB",
           targetfile = "103.pdf",
        urlaccessdate = "2024, May 07"
}