Facial Re-identification on Non-overlapping Cameras and in Uncontrolled Environments

  • Everardo Santiago RamírezEmail author
  • J. C. Acosta-Guadarrama
  • Jose Manuel Mejía Muñoz
  • Josue Dominguez Guerrero
  • J. A. Gonzalez-Fraga
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11524)


Face re-identification is an essential task in automatic video surveillance where the identity of the person is known previously. It aims to verify if other cameras have observed a specific face detected by a camera. However, this is a challenging task because of the reduced resolution, and changes in lighting and background available in surveillance video sequences. Furthermore, the face to get re-identified suffers changes in appearance due to expression, pose, and scale. Algorithms need robust descriptors to perform re-identification under these challenging conditions. Among various types of approaches available, correlation filters have properties that can be exploited to achieve a successful re-identification. Our proposal makes use of this approach to exploit both the shape and content of more representative facial images captured by a camera in a field of view. The resulting correlation filters can characterize the face of a person in a field of view; they are good at discriminating faces of different people, tolerant to variable illumination and slight variations in the rotation (in/out of plane) and scale. Further, they allow identifying a person from the first time that has appeared in the camera network. Matching the correlation filters generated in the field of views allows establishing a correspondence between the faces of the same person viewed by different cameras. These results show that facial re-identification under real-world surveillance conditions and biometric context can be successfully performed using correlation filters adequately designed.


Face re-identification and recognition Biometrics Correlation filters 


  1. 1.
    An, L., Kafai, M., Bhanu, B.: Dynamic Bayesian network for unconstrained face recognition in surveillance camera networks. IEEE J. Emerg. Sel. Top. Circ. Syst. 3(2), 155–164 (2013). Scholar
  2. 2.
    Apicella, A., Isgrò, F., Riccio, D.: Improving face recognition in low quality video sequences: single frame vs multi-frame super-resolution. In: Battiato, S., Gallo, G., Schettini, R., Stanco, F. (eds.) ICIAP 2017. LNCS, vol. 10484, pp. 637–647. Springer, Cham (2017). Scholar
  3. 3.
    Bäuml, M., Bernardin, K., Fischer, M., Ekenel, H.K., Stiefelhagen, R.: Multi-pose face recognition for person retrieval in camera networks. In: Proceedings–IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2010 (i), pp. 441–447 (2010).
  4. 4.
    Bäuml, M., Tapaswi, M., Stiefelhagen, R.: A time pooled track kernel for person identification. In: Proceedings of the 11th International Conference on Advanced Video and Signal-Based Surveillance (AVSS). IEEE, 26–28 August 2014Google Scholar
  5. 5.
    Bedagkar-Gala, A., Shah, S.K.: A survey of approaches and trends in person re-identification. Image Vis. Comput. 32(4), 270–286 (2014). Scholar
  6. 6.
    Casasent, D., Chang, W.T.: Correlation synthetic discriminant functions. Appl. Opt. 25, 2343–2350 (1986)CrossRefGoogle Scholar
  7. 7.
    Chen, Y., Duffner, S., Stoian, A., Dufour, J.Y., Baskurt, A.: Deep and low-level feature based attribute learning for person re-identification. Image Vis. Comput. 79, 25–34 (2018). Scholar
  8. 8.
    Cui, Z., Chang, H., Shan, S., Ma, B., Chen, X.: Joint sparse representation for video-based face recognition. Neurocomputing 135, 306–312 (2014). Scholar
  9. 9.
    Gong, S., Cristani, M., Loy, C.C., Hospedales, T.M.: The re-identification challenge. In: Gong, S., Cristani, M., Yan, S., Loy, C.C. (eds.) Person Re-Identification. ACVPR, pp. 1–20. Springer, London (2014). Scholar
  10. 10.
    Li, W., Zhu, X., Gong, S.: Person re-identification by deep joint learning of multi-loss classification. CoRR abs/1705.04724 (2017).
  11. 11.
    Liu, Z., Zhang, Z., Wu, Q., Wang, Y.: Enhancing person re-identification by integrating gait biometric. In: Jawahar, C.V., Shan, S. (eds.) ACCV 2014. LNCS, vol. 9008, pp. 35–45. Springer, Cham (2015). Scholar
  12. 12.
    Ma, B., Su, Y., Jurie, F.: Covariance descriptor based on bio-inspired features for person re-identification and face verification. Image Vis. Comput. 32(6), 379–390 (2014). Scholar
  13. 13.
    Ren, Y., Li, X., Lu, X.: Feedback mechanism based iterative metric learning for person re-identification. Pattern Recognit. 75, 1339–1351 (2018). Scholar
  14. 14.
    Ruchay, A., Kober, V., Gonzalez-Fraga, J.A.: Reliable recognition of partially occluded objects with correlation filters. Math. Probl. Eng. 2018, 8 p. (2018). Article ID 8284123
  15. 15.
    Santiago-Ramirez, E., Gonzalez-Fraga, J.A., Lazaro-Martnez, S.: Face recognition and tracking using unconstrained non-linear correlation filters. In: International Meeting of Electrical Engineering Research ENIINVIE (2012)Google Scholar
  16. 16.
    Santiago-Ramirez, E., Gonzalez-Fraga, J.A., Gutierrez, E., Alvarez-Xochihua, O.: Optimization-based methodology for training set selection to synthesize composite correlation filters for face recognition. Signal Process.: Image Commun. 43, 54–67 (2016). Scholar
  17. 17.
    Soleymani, R., Granger, E., Fumera, G.: Progressive boosting for class imbalance and its application to face re-identification. Expert. Syst. Appl. 101, 271–291 (2018). Scholar
  18. 18.
    Vijaya-Kumar, B.V.K., Mahalanobis, A., Juday, R.: Correlation Pattern Recognition. Cambridge University Press, Cambridge (2005)CrossRefGoogle Scholar
  19. 19.
    Wang, G., Zheng, F., Shi, C., Xue, J.H., Liu, C., He, L.: Embedding metric learning into set-based face recognition for video surveillance. Neurocomputing 151(P3), 1500–1506 (2015). Scholar
  20. 20.
    Wang, J., Zhou, S., Wang, J., Hou, Q.: Deep ranking model by large adaptive margin learning for person re-identification. Pattern Recognit. 74, 241–252 (2017). Scholar
  21. 21.
    Wang, J., Wang, Z., Liang, C., Gao, C., Sang, N.: Equidistance constrained metric learning for person re-identification. Pattern Recognit. 74, 38–51 (2018). Scholar
  22. 22.
    Wang, Q., Alfalou, A., Brosseau, C.: New perspectives in face correlation research: a tutorial. Adv. Opt. Photon. 9(1), 1–78 (2017). Scholar
  23. 23.
    Wang, Y., Shen, J., Petridis, S., Pantic, M.: A real-time and unsupervised face re-identification system for human-robot interaction. Pattern Recognit. Lett. (2018). Scholar
  24. 24.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004). Scholar
  25. 25.
    Watson, G., Bhalerao, A.: Person re-identification using deep foreground appearance modeling. J. Electron. Imaging 27 (2018).
  26. 26.
    Wong, Y., Chen, S., Mau, S., Sanderson, C., Lovell, B.C.: Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition. In: IEEE Biometrics Workshop, Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 81–88. IEEE, June 2011Google Scholar
  27. 27.
    Yang, B., Chen, S.: A comparative study on local binary pattern (LBP) based face recognition: LBP histogram versus LBP image. Neurocomputing 120, 365–379 (2013). Image Feature Detection and DescriptionCrossRefGoogle Scholar
  28. 28.
    Zheng, L., Yang, Y., Hauptmann, A.G.: Person re-identification: past, present and future. CoRR 14(8), 1–20 (2016)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Instituto de Ingeniería y TecnologíaUniversidad Autónoma de Ciudad JuárezCiudad JuárezMexico
  2. 2.Facultad de CienciasUniversidad Autónoma de Baja CaliforniaEnsenadaMexico

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