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Confidence Measure for Experimental Automatic Face Recognition System

  • Pavel KrálEmail author
  • Ladislav Lenc
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8946)

Abstract

This paper deals with automatic face recognition in order to propose and implement an experimental face recognition system. It will be used to automatically annotate photographs taken in completely uncontrolled environment. Recognition accuracy of such a system can be improved by identification of incorrectly classified samples in the post-processing step. However, this step is usually missing in current systems. In this work, we would like to solve this issue by proposing and integrating a confidence measure module to identify incorrectly classified examples. We propose a novel confidence measure approach which combines four partial measures by a multi-layer perceptron. Two individual measures are based on the posterior probability and two other ones use the predictor features. The experimental results show that the proposed system is very efficient, because almost all erroneous examples are successfully detected.

Keywords

Face recognition Czech News Agency Confidence measure Multi-layer perceptron Scale Invariant Feature Transform (SIFT) 

Notes

Acknowledgements

This work has been partly supported by the UWB grant SGS-2013-029 Advanced Computer and Information Systems and by the European Regional Development Fund (ERDF), project “NTIS - New Technologies for Information Society”, European Centre of Excellence, CZ.1.05/1.1.00/02.0090. We also would like to thank Czech New Agency (ČTK) for support and for providing the photographic data.

References

  1. 1.
    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). http://dx.doi.org/10.1007/978-3-540-24670-1_36 CrossRefGoogle Scholar
  2. 2.
    Aly, M.: Face recognition using sift features (2006)Google Scholar
  3. 3.
    Bartlett, M.S., Movellan, J.R., Sejnowski, T.J.: Face recognition by independent component analysis. IEEE Trans. Neural Netw. 13, 1450–1464 (2002)CrossRefGoogle Scholar
  4. 4.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Comput. Vis. Image Underst. 110(3), 346–359 (2008). doi: 10.1016/j.cviu.2007.09.014 CrossRefGoogle Scholar
  5. 5.
    Beham, M.P., Roomi, S.M.M.: A review of face recognition methods. Int. J. Pattern Recogn. Artif. Intell. 27(4), 1–35 (2013)CrossRefGoogle Scholar
  6. 6.
    Belhumeur, P.N., Hespanha, J.A.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)CrossRefGoogle Scholar
  7. 7.
    Bolme, D.S.: Elastic bunch graph matching. Ph.D. thesis, Colorado State University (2003)Google Scholar
  8. 8.
    Brown, C.D., Davis, H.T.: Receiver operating characteristics curves and related decision measures: a tutorial. Chemometr. Intell. Lab. Syst. 80(1), 24–38 (2006)CrossRefGoogle Scholar
  9. 9.
    Campadelli, P., Lanzarotti, R.: A face recognition system based on local feature characterization. In: Tistarelli, M., Bigun, J., Grosso, E. (eds.) Advanced Studies in Biometrics. LNCS, vol. 3161, pp. 147–152. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Deng, J., Schuller, B.: Confidence measures in speech emotion recognition based on semi-supervised learning. In: INTERSPEECH (2012)Google Scholar
  11. 11.
    Eickeler, S., Jabs, M., Rigoll, G.: Comparison of confidence measures for face recognition. In: FG, pp. 257–263. IEEE Computer Society (2000). http://dblp.uni-trier.de/db/conf/fgr/fg2000.html#EickelerJR00
  12. 12.
    Hu, X., Mordohai, P.: A quantitative evaluation of confidence measures for stereo vision. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2121–2133 (2012)CrossRefGoogle Scholar
  13. 13.
    Huang, K., Aviyente, S.: Sparse representation for signal classification. Adv. Neural Inf. Process. Syst. 19, 609 (2007)Google Scholar
  14. 14.
    Jiang, H.: Confidence measures for speech recognition: a survey. Speech Commun. 45(4), 455–470 (2005)CrossRefGoogle Scholar
  15. 15.
    Kepenekci, B.: Face recognition using Gabor wavelet transform. Ph.D. thesis, The Middle East Technical University (2001)Google Scholar
  16. 16.
    Križaj, J., Štruc, V., Pavešić, N.: Adaptation of SIFT features for robust face recognition. In: Campilho, A., Kamel, M. (eds.) ICIAR 2010. LNCS, vol. 6111, pp. 394–404. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  17. 17.
    Lenc, L., Král, P.: Confidence measure for automatic face recognition. In: International Conference on Knowledge Discovery and Information Retrieval. Paris, France, 26–29 October 2011Google Scholar
  18. 18.
    Lenc, L., Král, P.: Novel matching methods for automatic face recognition using SIFT. In: Iliadis, L., Maglogiannis, I., Papadopoulos, H. (eds.) Artificial Intelligence Applications and Innovations. IFIP AICT, vol. 381, pp. 254–263. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  19. 19.
    Lenc, L., Král, P.: Face recognition under real-world conditions. In: International Conference on Agents and Artificial Intelligence. Barcelona, Spain, 14–18 February 2013Google Scholar
  20. 20.
    Li, F., Wechsler, H.: Open world face recognition with credibility and confidence measures. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 462–469. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  21. 21.
    Li, W., Fu, P., Zhou, L.: Face recognition method based on dynamic threshold local binary pattern. In: Proceedings of the 4th International Conference on Internet Multimedia Computing and Service, pp. 20–24. ACM (2012)Google Scholar
  22. 22.
    Martínez, A.M.: Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class. IEEE Trans. Pattern Anal. Mach. Intell. 24(6), 748–763 (2002)CrossRefGoogle Scholar
  23. 23.
    Marukatat, S., Artières, T., Gallinari, P., Dorizzi, B.: Rejection measures for handwriting sentence recognition. In: Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition, 2002, pp. 24–29. IEEE (2002)Google Scholar
  24. 24.
    Poon, B., Amin, M.A., Yan, H.: Performance evaluation and comparison of pca based human face recognition methods for distorted images. Int. J. Mach. Learn. Cybernet. 2(4), 245–259 (2011)CrossRefGoogle Scholar
  25. 25.
    Powers, D.: Evaluation: from precision, recall and F-measure to ROC., informedness, markedness & correlation. J. Mach. Learn. Technol. 2(1), 37–63 (2011)MathSciNetGoogle Scholar
  26. 26.
    Proedrou, K., Nouretdinov, I., Vovk, V., Gammerman, A.J.: Transductive confidence machines for pattern recognition. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) ECML 2002. LNCS (LNAI), vol. 2430, pp. 381–390. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  27. 27.
    Senay, G., Linares, G., Lecouteux, B.: A segment-level confidence measure for spoken document retrieval. In: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5548–5551. IEEE (2011)Google Scholar
  28. 28.
    Servin, B., de Givry, S., Faraut, T.: Statistical confidence measures for genome maps: application to the validation of genome assemblies. Bioinformatics 26(24), 3035–3042 (2010)CrossRefGoogle Scholar
  29. 29.
    Shen, L.: Recognizing faces - an approach based on Gabor wavelets. Ph.D. thesis, University of Nottingham (2005)Google Scholar
  30. 30.
    Shen, L., Bai, L.: A review on gabor wavelets for face recognition. Pattern Anal. Appl. 9, 273–292 (2006)MathSciNetCrossRefGoogle Scholar
  31. 31.
    Sukkar, R.A.: Rejection for connected digit recognition based on gpd segmental discrimination. In: 1994 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1994, ICASSP 1994, vol. 1, pp. I-393. IEEE (1994)Google Scholar
  32. 32.
    Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. 19(6), 1635–1650 (2010)MathSciNetCrossRefGoogle Scholar
  33. 33.
    Timo, A., Hadid, A., Pietikinen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28, 2037–2041 (2006)CrossRefGoogle Scholar
  34. 34.
    Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (1991)Google Scholar
  35. 35.
    Wagner, A., Wright, J., Ganesh, A., Zhou, Z., Mobahi, H., Ma, Y.: Toward a practical face recognition system: Robust alignment and illumination by sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 34(2), 372–386 (2012)CrossRefGoogle Scholar
  36. 36.
    Wessel, F., Schluter, R., Macherey, K., Ney, H.: Confidence measures for large vocabulary continuous speech recognition. IEEE Trans. Speech Audio Process. 9(3), 288–298 (2001)CrossRefGoogle Scholar
  37. 37.
    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(2), 533–544 (2010)MathSciNetCrossRefGoogle Scholar
  38. 38.
    Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: a literature survey. ACM Comput. Surv. (CSUR) 35(4), 399–458 (2003)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer Science and Engineering, Faculty of Applied SciencesUniversity of West BohemiaPlzeňCzech Republic
  2. 2.New Technologies for the Information Society, Faculty of Applied SciencesUniversity of West BohemiaPlzeňCzech Republic

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