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Fusion of appearance-based face recognition algorithms

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Abstract

Although many algorithms have been proposed, face recognition and verification systems can guarantee a good level of performances only for controlled environments. In order to improve the performance and robustness of face recognition and verification systems, multi-modal and mono-modal systems based on the fusion of multiple recognisers using different or similar biometrics have been proposed, especially for verification purposes. In this paper, a recognition and verification system based on the combination of two well-known appearance-based representations of the face, namely, principal component analysis (PCA) and linear discriminant analysis (LDA), is proposed. Both PCA and LDA are used as feature extractors from frontal view images. The benefits of such a fusion are shown for different environmental conditions, namely, ideal conditions, characterised by a very limited variability of environmental parameters, and real conditions with a large variability of lighting, scale and facial expression.

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References

  1. Jain AK, Bolle R, Pankanti S (eds) (1999) Biometrics: Personal identification in networked society. Kluwer Academic Publishers, Boston/Dordrecht/London

  2. Wechsler H, Phillips JP, Bruce V, Folgeman Soulie F, Huang TS (eds) (1997) Face recognition – From theory to applications. ASI NATO series, vol 163, Springer, Berlin Heidelberg New York

  3. Yang MH, Kriegman D, Ahuja N (2002) Detecting face images: A survey. IEEE T Pattern Anal 24(1):24–58

    Google Scholar 

  4. Ross A, Jain AK (2003) Information fusion in biometrics. Pattern Recogn Lett 24(13):2115–2125

    Article  Google Scholar 

  5. Verlinde P, Druyts P, Chollet G, Acheroy M (1999) A multi-level data fusion approach for gradually upgrading the performances of identity verification systems. In: Dasarathy B (ed) Sensor fusion: Architectures, algorithms and application III, vol 3719. SPIE Press, Orlando, Florida, USA, pp 14–25

  6. Achermann B, Bunke H (1996) Combination of classifiers on the decision level for face recognition. Technical report IAM-96–002, Institut für Informatik und angewandte Mathematik, Universität Bern, January 1996

  7. Zhao WY, Chellappa R, Rosenfeld A, Philips PJ (2000) Face recognition: A literature survey. UMD CfAR technical report CAR-TR-948

    Google Scholar 

  8. Turk M, Pentland A (1991) Eigenfaces for face recognition. J Cognitive Neurosci 3(1):71–86

    Google Scholar 

  9. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE T Pattern Anal 19(7):711–720

    Article  Google Scholar 

  10. Wiskott L, Fellous JM, Krüger N, von der Malsburg C (1997) Face recognition by elastic bunch graph matching. IEEE T Pattern Anal 19(7):775–779

    Article  Google Scholar 

  11. Kotropoulos CL, Tefas A, Pitas I (2000) Morphological elastic graph matching applied to frontal face authentication under well-controlled and real conditions. Pattern Recogn 33(12):1935–1947

    Article  MATH  Google Scholar 

  12. Penev PS, Atick J (1996) Local feature analysis: A general statistical theory for object representation. Network–Comp Neural 7(3):477–500

    Google Scholar 

  13. de Vel O, Aeberhard S (1999) Line-based face recognition under varying pose. IEEE T Pattern Anal 21(10):1081–1088

    Article  Google Scholar 

  14. Roli F, Kittler J (eds) (2002) Multiple classifier systems, Springer, Berlin Heidelberg New York LNCS 2364

  15. Yu K, Yang XJ, Bunke H (1995) Face recognition by facial profile analysis. In: Bichsel M (ed) Proceedings of the international workshop on automatic face and gesture recognition, Zurich, Switzerland, June 1995, pp 208–213

  16. Lucas SM (1997) The continuous n-tuple classifier and its application to face recognition. Electron Lett 33:1676–1678

    Article  Google Scholar 

  17. Lin S, Kung S, Lin L (1997) Face recognition/detection by a probabilistic decision-based neural network. IEEE T Neural Networ 8(1):114–132

    Article  MATH  Google Scholar 

  18. Lawrence S, Giles C, Tsoi A, Back A (1997) Face recognition: a convolutional neural network approach. IEEE T Neural Networ 8(1):98–113

    Article  MATH  Google Scholar 

  19. Tolba AS, Abu-Rezq AN (2000) Combined classifier for invariant face recognition. Pattern Anal Appl 3(4):289–302

    Article  MATH  Google Scholar 

  20. Roli F, Fumera G, Kittler J (2002) Fixed and trained combiners for fusion of unbalanced pattern classifiers. In: Proceedings of the 5th international conference on information fusion (IF 2002), Annapolis, Maryland (USA), 7–11 July 2002, pp 278–284

  21. Tan T, Yan H (1999) Face recognition by fractal transformation. In: Proceedings of the IEEE ICASSP, Turkey, May 1999, pp 3537–3540

  22. Messer K, Matas J, Kittler J, Luettin J, Maitre G (1999) XM2VTSdb: The extended M2VTS database. In: Proceedings of the 2nd international conference on audio- and video-based biometric person authentication, Washington, DC, March 1999

  23. Luettin J, Matre G (1998) Evaluation protocol for the extended m2vts database (xm2vtsdb). Technical report IDIAP-COM 98–05, Dalle Mole Institute for Perceptual Artificial Intelligence,http://www.idiap.ch, July 1998

  24. Marcialis GL, Roli F (2003) Fusion of face recognition algorithms for video-based surveillance systems, in multisensor surveillance systems: The Fusion Perspective. In: Foresti GL, Regazzoni C, Varshney P (eds) (2003) Kluwer Academic Publishers, Boston/Dordrecht/London, pp 235–250

  25. Marcialis GL, Roli F (2002) Fusion of LDA and PCA for face verification. In: Tistarelli M, Bigun J, Jain AK (eds) (2002) Proceedings of the workshop on biometric authentication, Copenhagen, Denmark, June 2002, Springer, Berlin Heidelberg New York, LNCS 2359, pp 30–37

  26. Duda RO, Hart PE, Stork DG (2001) Pattern Classification, Wiley, USA

  27. Zhao W, Krishnaswamy A, Chellappa R, Swets D, Weng J (1998) Discriminant analysis of principal components for face recognition. In: Wechsler H, Phillips PJ, Bruce V, Soulie FF, Huang TS (eds) Face recognition: From theory to applications. Springer, Berlin Heidelberg New York, pp 73–85

  28. Chen LF, Liao HYM, Ko MT, Lin JC, Yu GJ (2000) A new LDA-based face recognition system which can solve the small sample size problem. Pattern Recogn 33:1713–1726

    Article  Google Scholar 

  29. Goudail F, Lange E, Iwamoto T, Kyuma K, Otsu O (1996) Face recognition system using local autocorrelation and multiscale integration. IEEE T Pattern Anal 18(10):1024–1028

    Article  Google Scholar 

  30. Fukunaga K (1990) Introduction of statistical pattern recognition, Academic Press, New York

  31. Liu K, Cheng YQ, Yang JY, Liu X (1992) A generalised optimal set of discriminant vectors. Pattern Recogn 25(7):731–729

    Article  Google Scholar 

  32. Yang J, Yang J (2003) Why can LDA be performed in PCA transformed space? Pattern Recogn 36(2):563–566

    Article  Google Scholar 

  33. Kittler J, Li YP, Matas J (2000) On matching scores for LDA-based face verification. In: Mirmehdi M, Thomas B (eds) Proceedings of the British Machine Vision Conference 2000, Bristol, UK, September 2000, pp 42–51

  34. Martinez AM, Kak AC (2001) PCA versus LDA. IEEE T Pattern Anal 23(2):228–233

    Article  Google Scholar 

  35. Mansfield T, Wayman J (2002) Best practices in testing and reporting performance of biometric devices. For the Biometrics Working Group. Technical report issue 2 draft 9, Centre for Mathematics and Scientific Computing, National Physics Laboratory, Middlesex, UK, February 2002

  36. Lu X, Wang Y, Jain AK (2003) Combining classifiers for face recognition, Proc. ISME, IEEE Int. Cont. on Multimedia & Expo, vol. III, pp. 13–16, Baltimore, MD, July 6–9, 2003

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Acknowledgments

This work was partially supported by the Italian Ministry of University and Scientific Research (MIUR) in the framework of the research project on distributed systems for multisensor recognition with augmented perception for ambient security and customisation.

The authors also wish to thank the anonymous referees for the useful and constructive comments and suggestions, which allowed them to substantially improve the paper.

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Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi, 09123, Cagliari, Italy

    Gian Luca Marcialis & Fabio Roli

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  1. Gian Luca Marcialis
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  2. Fabio Roli
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Correspondence to Fabio Roli.

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Marcialis, G.L., Roli, F. Fusion of appearance-based face recognition algorithms. Pattern Anal Applic 7, 151–163 (2004). https://doi.org/10.1007/s10044-004-0212-7

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