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Improving Face Recognition Accuracy for Brazilian Faces in a Criminal Investigation Department

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Intelligent Systems (BRACIS 2020)


This work addresses a critical problem in the use of the Face Recognition (FR) task by a police department state of Brazil. FR is a valuable crime-fighting tool that can help the police service prevent and detect crime, preserve public safety, and bring offenders to justice. Although significant advances have been shown in the last years, the works are based on large labeled datasets and supervised training. But with this approach, the lack of representative data distribution is an issue, known as data bias, mainly according to some aspects that makes FR harders: gender and race. Recent works have suggested that these two aspects may cause a significant accuracy drop. Thus, the paper is concerned over the FR data bias problem for Brazilian faces. Using pre-trained models learned from public datasets, we demonstrate that even in the small training dataset, it is possible to improve the accuracy of Brazilian faces with simple yet effective implementation tricks in fine-tuning. Two important conclusions wast obtained from this study using a non-public police dataset. First, there is a strong suggestion of data bias concerning ethnicity when evaluating models trained with public datasets on Brazilian faces, and second, the fine-tuning task implemented over non-public police dataset showed a relevant improvement to minimize the dataset bias problem.

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Correspondence to Jones José da Silva Júnior .

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da Silva Júnior, J.J., Soares, A.S. (2020). Improving Face Recognition Accuracy for Brazilian Faces in a Criminal Investigation Department. In: Cerri, R., Prati, R.C. (eds) Intelligent Systems. BRACIS 2020. Lecture Notes in Computer Science(), vol 12319. Springer, Cham.

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