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Unconstrained Facial Images: Database for Face Recognition Under Real-World Conditions

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Advances in Artificial Intelligence and Its Applications (MICAI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9414))

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Abstract

The objective of this paper is to introduce a novel face database. It is composed of face images taken in real-world conditions and is freely available for research purposes at http://ufi.kiv.zcu.cz. We have created this dataset in order to facilitate to researchers a straightforward comparison and evaluation of their face recognition approaches under “very difficult” conditions. It is composed of two partitions. The first one, called Cropped images, contains automatically detected faces from photographs. The number of individuals is 605. These images are cropped and resized to have approximately the same face size. Images in the second partition, called Large images, contain not only faces, however some background objects are also present. Therefore, it is necessary to include the face detection task before the face recognition itself. This partition contains images of 530 individuals. Another contribution of this paper is to show the recognition accuracy of several state-of-the-art face recognition approaches on this dataset to provide a baseline score for further research.

This work has been partly supported by the project LO1506 of the Czech Ministry of Education, Youth and Sports. We would like also to thank Czech New Agency (ČTK) for support and for providing the data.

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Notes

  1. 1.

    http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html.

  2. 2.

    http://www2.ece.ohio-state.edu/~aleix/ARdatabase.html.

  3. 3.

    http://www.ctk.eu/.

  4. 4.

    http://ufi.kiv.zcu.cz.

References

  1. Phillips, P.J., Moon, H., Rizvi, S., Rauss, P.J., et al.: The feret evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22, 1090–1104 (2000)

    Article  Google Scholar 

  2. Delac, K., Grgic, M., Grgic, S.: Independent comparative study of PCA, ICA, and LDA on the feret data set. Int. J. Imaging Syst. Technol. 15, 252 (2005)

    Article  Google Scholar 

  3. Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical report, Technical report 07–49, University of Massachusetts, Amherst (2007)

    Google Scholar 

  4. Hasan, M.K., Pal, C.: Experiments on visual information extraction with the faces of wikipedia. In: Twenty-Eighth AAAI Conference on Artificial Intelligence (2014)

    Google Scholar 

  5. Grgic, M., Delac, K., Grgic, S.: Scface-surveillance cameras face database. Multimedia Tools Appl. 51, 863–879 (2011)

    Article  Google Scholar 

  6. Ng, H.W., Winkler, S.: A data-driven approach to cleaning large face datasets. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 343–347. IEEE (2014)

    Google Scholar 

  7. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001. vol. 1. pp. I-511. IEEE (2001)

    Google Scholar 

  8. Sim, T., Baker, S., Bsat, M.: The CMU pose, illumination, and expression (pie) database. In: Proceedings Fifth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 46–51. IEEE (2002)

    Google Scholar 

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

    Article  Google Scholar 

  10. Georghiades, A., et al.: Yale face database. Center for computational Vision and Control at Yale University (1997). http://cvc.yale.edu/projects/yalefaces/yalefa

  11. Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intell. 23, 643–660 (2001)

    Article  Google Scholar 

  12. Jain, A.K., Li, S.Z. (eds.): Handbook of Face Recognition, vol. 1. Springer, London (2005)

    MATH  Google Scholar 

  13. Gao, W., Cao, B., Shan, S., Chen, X., Zhou, D., Zhang, X., Zhao, D.: The cas-peal large-scale chinese face database and baseline evaluations. IEEE Trans. Syst. Man Cybernetics Part A Syst. Hum. 38, 149–161 (2008)

    Article  Google Scholar 

  14. Bailly-Bailliére, E., et al.: The banca database and evaluation protocol. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 625–638. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  15. Kumar, N., Berg, A.C., Belhumeur, P.N., Nayar, S.K.: Attribute and simile classifiers for face verification. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 365–372. IEEE (2009)

    Google Scholar 

  16. Gross, R.: Face databases. In: Handbook of Face Recognition, pp. 301–327. Springer (2005)

    Google Scholar 

  17. Lenc, L., Král, P.: Automatic face recognition system based on the SIFT features. Comput. Electr. Eng. (2015)

    Google Scholar 

  18. Ahonen, T., Hadid, A., Pietikäinen, M.: Face recognition with local binary patterns. In: Pajdla, T., Matas, J.G. (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  19. Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29, 51–59 (1996)

    Article  Google Scholar 

  20. Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28, 2037–2041 (2006)

    Article  Google Scholar 

  21. Zhang, B., Gao, Y., Zhao, S., Liu, J.: Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor. IEEE Trans. Image Process. 19, 533–544 (2010)

    Article  MathSciNet  Google Scholar 

  22. Vu, N.S., Dee, H.M., Caplier, A.: Face recognition using the poem descriptor. Pattern Recogn. 45, 2478–2488 (2012)

    Article  Google Scholar 

  23. Lenc, L., Král, P.: Automatically detected feature positions for LBP based face recognition. In: Iliadis, L. (ed.) AIAI 2014. IFIP AICT, vol. 436, pp. 246–255. Springer, Heidelberg (2014)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of West Bohemia, Plzeň, Czech Republic

    Ladislav Lenc & Pavel Král

  2. NTIS - New Technologies for the Information Society, University of West Bohemia, Plzeň, Czech Republic

    Ladislav Lenc & Pavel Král

Authors
  1. Ladislav Lenc
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  2. Pavel Král
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Correspondence to Ladislav Lenc .

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Editors and Affiliations

  1. Unidad Profesional Interdisciplinaria, México DF, Mexico

    Obdulia Pichardo Lagunas

  2. Universidad Autónoma Metropolitana, México DF, Mexico

    Oscar Herrera Alcántara

  3. Instituto de Investigaciones Eléctricas, Cuernavaca, Morelos, Mexico

    Gustavo Arroyo Figueroa

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Lenc, L., Král, P. (2015). Unconstrained Facial Images: Database for Face Recognition Under Real-World Conditions. In: Pichardo Lagunas, O., Herrera Alcántara, O., Arroyo Figueroa, G. (eds) Advances in Artificial Intelligence and Its Applications. MICAI 2015. Lecture Notes in Computer Science(), vol 9414. Springer, Cham. https://doi.org/10.1007/978-3-319-27101-9_26

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  • DOI: https://doi.org/10.1007/978-3-319-27101-9_26

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-27101-9

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