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

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Intelligent Systems (BRACIS 2020)

Abstract

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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Notes

  1. 1.

    Amazon.com, Inc.

  2. 2.

    Brazilian Institute of Geography and Statistics.

References

  1. Institute of Automation, C.A.o.C.: CASIA WebFAce. http://www.cbsr.ia.ac.cn/english/CASIA-WebFace-Database.html. Acessado em abril de 2019

  2. Cao, Q., Shen, L., Xie, W., Parkhi, O.M., Zisserman, A.: VGGFace2: a dataset for recognising faces across pose and age (2017)

    Google Scholar 

  3. de Geografia e Estatística, I.B.: Censo demográfico do brasil. https://sidra.ibge.gov.br/Tabela/#resultado (2010). Acessado em 23 Mar 2020

  4. Guo, Y., Zhang, L., Hu, Y., He, X., Gao, J.: MS-Celeb-1M: a dataset and benchmark for large-scale face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 87–102. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_6

    Chapter  Google Scholar 

  5. Rawls, A.W., Ricanek, K.: MORPH: development and optimization of a longitudinal age progression database. In: Fierrez, J., Ortega-Garcia, J., Esposito, A., Drygajlo, A., Faundez-Zanuy, M. (eds.) BioID 2009. LNCS, vol. 5707, pp. 17–24. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04391-8_3

    Chapter  Google Scholar 

  6. Klar, B.F., et al.: Pushing the frontiers of unconstrained face detection and recognition: iarpa janus benchmark A (2015)

    Google Scholar 

  7. Nicholls, M.E., Churches, O., Loetscher, T.: Perception of an ambiguous figure is affected by own-age social biases. Sci. Rep. 8, 12661 (2018)

    Article  Google Scholar 

  8. Merler, M., Ratha, N., Feris, R.S., Smith, J.R.: Diversity in faces (2019)

    Google Scholar 

  9. Mitchell, T.M.: The need for biases in learning generalizations. Laboratory for Computer Science Research, Department of Computer Science (1980)

    Google Scholar 

  10. Nagpal, S., Singh, M., Singh, R., Vatsa, M., Ratha, N.: Deep learning for face recognition: pride or prejudiced? (2019)

    Google Scholar 

  11. Qian, N.: On the momentum term in gradient descent learning algorithms. Neural Netw. Official J. Int. Neural Netw. Soc. 12(1), 145–151 (1999)

    Article  MathSciNet  Google Scholar 

  12. Gross, R., Matthews, I., Cohn, J., Kanade, T., Baker, S.: Multi-PIE. Image Vis. Comput. 28, 807–813 (2010)

    Google Scholar 

  13. Raji, I.D., Buolamwini, J.: Actionable auditing: investigating the impact of publicly naming biased performance results of commercial AI products. In: AAAI ACM Conference on AI Ethics and Society (2019)

    Google Scholar 

  14. Rio, G.: Sistema de reconhecimento facial da pm do rj falha, e mulher é detida por engano (2019). https://g1.globo.com/rj/rio-de-janeiro/noticia/2019/07/11/sistema-de-reconhecimento-facial-da-pm-do-rj-falha-e-mulher-e-detida-por-engano.ghtml. Accessed 27 May 2020

  15. Sandberg, D.: Face recognition using Tensorflow. https://github.com/davidsandberg/facenet. Acessado em 01 Apr 2020

  16. Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering (2015)

    Google Scholar 

  17. Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.: Inception-v4, inception-resnet and the impact of residual connections on learning (2016)

    Google Scholar 

  18. The New York Times: How the police use facial recognition, and where it falls short (2020). https://www.nytimes.com/2020/01/12/technology/facial-recognition-police.html. Acessado em 04 Apr 2020

  19. Wang, F., Liu, W., Liu, H., Cheng, J.: Additive margin softmax for face verification (2018)

    Google Scholar 

  20. Wang, M., Deng, W.: Deep face recognition: a survey. arXiv preprint arXiv:1804.06655 (2019)

  21. Wang, M., Deng, W., Hu, J., Tao, X., Huang, Y.: Racial faces in-the-wild: reducing racial bias by information maximization adaptation network (2018)

    Google Scholar 

  22. Weinberger, K.Q., Blitzer, J., Saul., L.K.: Distance metric learning for large margin nearest neighbor classification. MIT Press (2011)

    Google Scholar 

  23. Wen, Y., Zhang, K., Li, Z., Qiao, Yu.: A discriminative feature learning approach for deep face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 499–515. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_31

    Chapter  Google Scholar 

  24. Wu, X., He, R., Sun, Z., Tan, T.: A light CNN for deep face representation with noisy labels (2015)

    Google Scholar 

  25. Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Sig. Process. Lett. 23(10), 1499–1503 (2016). https://doi.org/10.1109/LSP.2016.2603342

    Article  Google Scholar 

  26. Zhuang, F., et al.: A comprehensive survey on transfer learning (2019)

    Google Scholar 

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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. https://doi.org/10.1007/978-3-030-61377-8_20

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  • DOI: https://doi.org/10.1007/978-3-030-61377-8_20

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