Comparative Analysis of Benchmark Datasets for Face Recognition Algorithms Verification

  • Paweł Forczmański
  • Magdalena Furman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7594)


The paper presents a problem of recognition of facial portraits in the aspect of benchmark database quality. The aim of the work presented here was to analyse the potential of datasets published over the Internet and the predicted applicability of such data for the task of face recognition performance verification. We gathered 41 datasets created and published by various academic and commercial bodies. In the paper we focus on both pure data characteristics, including the number of images, their spatial resolution, quality, content and usability, as well as more high-level properties, e.g. face orientation, expression, background, lighting, and attributes like hats, glasses and beards. We have chosen several datasets on which we performed more detailed experiments related to face recognition. We employed several database preparation algorithms (cross-validation based on different schemes) to make the results as much objective as possible. Here, Principal Component Analysis was employed, as a standard tool for dimensionality reduction. The classification was performed using simple Euclidean metrics. Performed experiments showed a true potential of selected databases.


Face Recognition Face Detection Gesture Recognition Independent Component Analysis Benchmark Dataset 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Gross, R.: Face Database. In: Li, S., Jain, A. (eds.) Handbook of Face Recognition, p. 395. Springer (2005)Google Scholar
  2. 2.
    Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neurosicence 3(1), 71–86 (1991)CrossRefGoogle Scholar
  3. 3.
    Bartlett, M.S., Movellan, J.R., Sejnowski, T.J.: Face recognition by independent component analysis. IEEE Trans. on Neural Networks 13(6), 1450–1464 (2002)CrossRefGoogle Scholar
  4. 4.
    Swets, D., Weng, J.: Using Discriminant Eigenfeatures for Image Retrieval. IEEE Trans. on Pattern Analysis and Machine Intelligence 18(8), 831–836 (1996)CrossRefGoogle Scholar
  5. 5.
    Hancock, P.: Psychological Image Collection at Stirling, PICS (2011),
  6. 6.
    Thomaz, C.E., Giraldi, G.A.: A new ranking method for Principal Components Analysis and its application to face image analysis. Image and Vision Comp. 28(6), 902–913 (2010)CrossRefGoogle Scholar
  7. 7.
    Solina, F., Peer, P., Batagelj, B., Juvan, S., Kovac, J.: Color-based face detection in the ”15 seconds of fame” art installation. In: Mirage 2003, Conference on Computer Vision / Computer Graphics Collaboration for Model-based Imaging, Rendering, image Analysis and Graphical special Effects, INRIA Rocquencourt, France, Wilfried Philips, Rocquencourt, INRIA, pp. 38–47 (2003)Google Scholar
  8. 8.
    Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments. University of Massachusetts, Amherst, Tech. Rep. 07-49 (2007)Google Scholar
  9. 9.
    Jain, V., Mukherjee, A.: The Indian Face Database (2002),
  10. 10.
    Phillips, P., Wechsler, H., Huang, J., Rauss, P.: The FERET database and evaluation procedure for face recognition algorithms. Image and Vision Comp. 16/5, 295–306 (1999)Google Scholar
  11. 11.
    Sim, T., Baker, S., Bsat, M.: The CMU Pose, Illumination, and Expression Database. IEEE TPAMI 25(12) (2003)Google Scholar
  12. 12.
    BioID-Technology Research. The BioID Face Database. (2001),
  13. 13.
    Milborrow, S., Morkel, J., Nicolls, F.: The MUCT Landmarked Face Database, Pattern Recognition Association of South Africa (2010),
  14. 14.
    Nordstrøm, M.M., Larsen, M., Sierakowski, J., Stegmann, M.B.: The IMM Face Database - An Annotated Dataset of 240 Face Images, Informatics and Mathematical Modelling, Technical University of Denmark, DTU (2004),
  15. 15.
    Spacek, L.: Collection of facial Images. Computer Vision Science Research Projects, University of Essex, Department of Computer Science, Essex (2008),
  16. 16.
    AT&T Laboratories Cambridge. Database of Faces (1994),
  17. 17.
    Weyrauch, B., Huang, J., Heisele, B., Blanz, V.: Component-based Face Recognition with 3D Morphable Models. In: First IEEE Workshop on Face Processing in Video, Washington, D.C. (2004)Google Scholar
  18. 18.
    Weber, M.: Frontal face dataset. California Institute of Technology (1999),
  19. 19.
    Georgia Tech Face Database (2011),
  20. 20.
    Gourier, N., Hall, D., Crowley, J.L.: Estimating Face Orientation from Robust Detection of Salient Facial Features Proceedings of Pointing. In: ICPR, International Workshop on Visual Observation of Deictic Gestures, Cambridge, UK (2004)Google Scholar
  21. 21.
    Minear, M., Park, D.C.: A lifespan database of adult facial stimuli. Behavior Research Methods, Instruments, & Computers 36, 630–633 (2004)CrossRefGoogle Scholar
  22. 22.
    Tarr, M.J.: Face-Place, Center for the Neural Basis of Cognition, Carnegie Mellon University (2008),
  23. 23.
    Lyons, M.J., Akamatsu, S., Kamachi, M., Gyoba, J.: Coding Facial Expressions with Gabor Wavelets Proceedings. In: Third IEEE Internat. Conference on Automatic Face and Gesture Recognition, pp. 200–205. IEEE Comp. Society, Nara Japan (1998)CrossRefGoogle Scholar
  24. 24.
    Graham, D.B., Allinson, N.M.: Characterizing Virtual Eigensignatures for General Purpose Face Recognition. In: Wechsler, H., Phillips, P.J., Bruce, V., Fogelman-Soulie, F., Huang, T.S. (eds.) Face Recognition: From Theory to Applications, NATO ASI Series F, Computer and Systems Sciences, vol. 163, pp. 446–456 (1998)Google Scholar
  25. 25.
    Messer, K., Matas, J., Kittler, J., Luettin, J., Maitre, G.: Xm2vtsdb: The extended m2vts database. In: Second Inter. Conf. of Audio and Video-based Biometric Person Authentication (1999)Google Scholar
  26. 26.
    The BANCA Database (2004),
  27. 27.
    Kasiński, A., Florek, A., Schmidt, A.: The PUT Face Database. Image Processing & Communications 13(3-4), 59–64 (2008)Google Scholar
  28. 28.
    Martinez, A.M., Benavente, R.: The AR Face Database. CVC Tech. Rep. #24 (1998)Google Scholar
  29. 29.
    Bellhumer, P.N., Hespanha, J., Kriegman, D.: Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE TPAMI, Special Issue on Face Recognition 17(7), 711–720 (1997)Google Scholar
  30. 30.
    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. Intelligence 23(6), 643–660 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Paweł Forczmański
    • 1
  • Magdalena Furman
    • 1
  1. 1.Faculty of Computer Science and Information TechnologyWest Pomeranian University of Technology, SzczecinSzczecinPoland

Personalised recommendations