International Conference on Image Analysis and Processing

ICIAP 2015: New Trends in Image Analysis and Processing -- ICIAP 2015 Workshops pp 59-68 | Cite as

Quis-Campi: Extending in the Wild Biometric Recognition to Surveillance Environments

  • João C. Neves
  • Gil Santos
  • Sílvio Filipe
  • Emanuel Grancho
  • Silvio Barra
  • Fabio Narducci
  • Hugo Proença
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9281)

Abstract

Efforts in biometrics are being held into extending robust recognition techniques to in the wild scenarios. Nonetheless, and despite being a very attractive goal, human identification in the surveillance context remains an open problem. In this paper, we introduce a novel biometric system – Quis-Campi – that effectively bridges the gap between surveillance and biometric recognition while having a minimum amount of operational restrictions. We propose a fully automated surveillance system for human recognition purposes, attained by combining human detection and tracking, further enhanced by a PTZ camera that delivers data with enough quality to perform biometric recognition. Along with the system concept, implementation details for both hardware and software modules are provided, as well as preliminary results over a real scenario.

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References

  1. 1.
    Bharadwaj, S., Bhatt, H., Vatsa, M., Singh, R.: Periocular biometrics: when iris recognition fails. In: 4th IEEE Int’l Conf. on Biometrics: Theory Applications and Systems (BTAS), 2010, pp. 1–6, September 2010Google Scholar
  2. 2.
    Bledsoe, W.W.: The model method in facial recognition. Tech. Report PRI 15, Panoramic Research Inc, Palo Alto, California (1964)Google Scholar
  3. 3.
    Castrillón, M., Dénis, O., Guerra, C., Hernández, M.: Encara2: Real-time detection of multiple faces at different resolutions in video streams. Journal of Visual Communication and Image Representation 18(2), 130–140 (2007)CrossRefGoogle Scholar
  4. 4.
    Daugman, J.: High confidence visual recognition of persons by a test of statistical independence. Pattern Analysis and Machine Intelligence 15(11), 1148–1161 (1993)CrossRefGoogle Scholar
  5. 5.
    Haritaoglu, I., Harwood, D., Davis, L.: W4: Real-time surveillance of people and their activities. Pattern Analysis and Machine Intelligence 22(8), 809–830 (2000)CrossRefGoogle Scholar
  6. 6.
    Jain, A., Pankanti, S., Prabhakar, S., Hong, L., Ross, A.: Biometrics: a grand challenge. In: Proc. of the 17th Int’l Conf. on Pattern Recognition (ICPR), vol. 2, pp. 935–942 (2004)Google Scholar
  7. 7.
    Kalman, R.E.: A New Approach to Linear Filtering and Prediction Problems. Trans. of the ASME - Journal of Basic Engineering, (82 (Series D)), 35–45 (1960)Google Scholar
  8. 8.
    Kamgar-Parsi, B., Lawson, W., Kamgar-Parsi, B.: Toward development of a face recognition system for watchlist surveillance. Pattern Analysis and Machine Intelligence 33(10), 1925–1933 (2011)CrossRefGoogle Scholar
  9. 9.
    Keni, B., Rainer, S.: Evaluating multiple object tracking performance: the clear mot metrics. EURASIP Journal on Image and Video Processing (2008)Google Scholar
  10. 10.
    Maddalena, L., Petrosino, A.: A self-organizing approach to background subtraction for visual surveillance applications. IEEE Trans. on Image Processing 17(7), 1168–1177 (2008)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Matey, J., Naroditsky, O., Hanna, K., Kolczynski, R., LoIacono, D., Mangru, S., Tinker, M., Zappia, T., Zhao, W.: Iris on the move: Acquisition of images for iris recognition in less constrained environments. Proc. of the IEEE 94, 1936–1947 (2006)CrossRefGoogle Scholar
  12. 12.
    Park, U., Ross, A., Jain, A.: Periocular biometrics in the visible spectrum: a feasibility study. In: IEEE 3rd Int’l Conf. on Biometrics: Theory, Applications, and Systems (BTAS), pp. 1–6, September 2009Google Scholar
  13. 13.
    Shi, J., Tomasi, C.: Good features to track. In: IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, pp. 593–600. IEEE (1994)Google Scholar
  14. 14.
    Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. IEEE Comp. Soc. Conf. on Computer Vision and Pattern Recognition 2, 252 (1999)Google Scholar
  15. 15.
    Neves, J.C., Moreno, J.C., Barra, S., Proença, H.: A calibration algorithm for multi-camera visual surveillance systems based on single-view metrology. In: Paredes, R., Cardoso, J.S., Pardo, X.M. (eds.) IbPRIA 2015. LNCS, vol. 9117, pp. 552–559. Springer, Heidelberg (2015) CrossRefGoogle Scholar
  16. 16.
    Neves, J.C., Proença, H.: Dynamic camera scheduling for visual surveillance in crowded scenes using Markov random fields. In: Proc. of the 12th IEEE Int’l Conf. on Advanced Video and Signal based Surveillance (AVSS) (2015)Google Scholar
  17. 17.
    Turk, M., Pentland, A.: Face recognition using eigenfaces. In: IEEE Computer Society Conf. on Computer Vision and Pattern Recognition. Proc. CVPR 1991, pp. 586–591 (1991)Google Scholar
  18. 18.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proc. of the 2001 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, vol. 1, pp. 511–518 (2001)Google Scholar
  19. 19.
    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. Pattern Analysis and Machine Intelligence 34(2), 372–386 (2012)CrossRefGoogle Scholar
  20. 20.
    Zhao, W., Chellappa, R., Phillips, P., Rosenfeld, A.: Face recognition: A literature survey. ACM Computing Surveys 35(4), 399–458 (2000)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • João C. Neves
    • 1
  • Gil Santos
    • 1
  • Sílvio Filipe
    • 1
  • Emanuel Grancho
    • 1
  • Silvio Barra
    • 2
  • Fabio Narducci
    • 3
  • Hugo Proença
    • 1
  1. 1.Department of Computer Science, IT - Instituto de TelecomunicaçõesUniversity of Beira InteriorCovilhãPortugal
  2. 2.DMI - Dipartimento di Matematica e InformaticaUniversity of CagliariCagliaryItaly
  3. 3.DISTRA-MITUniversity of SalernoFiscianoItaly

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