Multimedia Tools and Applications

, Volume 51, Issue 3, pp 863–879 | Cite as

SCface – surveillance cameras face database

  • Mislav GrgicEmail author
  • Kresimir Delac
  • Sonja Grgic


In this paper we describe a database of static images of human faces. Images were taken in uncontrolled indoor environment using five video surveillance cameras of various qualities. Database contains 4,160 static images (in visible and infrared spectrum) of 130 subjects. Images from different quality cameras should mimic real-world conditions and enable robust face recognition algorithms testing, emphasizing different law enforcement and surveillance use case scenarios. In addition to database description, this paper also elaborates on possible uses of the database and proposes a testing protocol. A baseline Principal Component Analysis (PCA) face recognition algorithm was tested following the proposed protocol. Other researchers can use these test results as a control algorithm performance score when testing their own algorithms on this dataset. Database is available to research community through the procedure described at


Video surveillance cameras Face database Face recognition 



The authors would like to thank Bozidar Klimpak for his technical support during database acquistion and indexing, Kresimir Marusic and Tehnozavod Marusic Ltd. for providing surveillance system equipment, Boris Krzic for providing professional photo equipment and his help with mug shots capturing, Darko Poljak for developing and providing JAVA software used for semiautomatic determination of eyes, nose and mouth coordinates, and to all participants in this project. Portions of the research in this paper use the FERET database of facial images collected under the FERET program. The authors would like to thank the FERET Technical Agent, the U.S. National Institute of Standards and Technology (NIST) for providing the FERET database. The work described in this paper was conducted under the research project “Intelligent Image Features Extraction in Knowledge Discovery Systems” (036-0982560-1643), supported by the Ministry of Science, Education and Sports of the Republic of Croatia.


  1. 1.
    Abatea AF, Nappi M, Riccioa D, Sabatino G (2007) 2D and 3D face recognition: a survey. Pattern Recogn Lett 28(14):1885–1906CrossRefGoogle Scholar
  2. 2.
    ANSI INCITS 385-2004 (2004) Face Recognition Format for Data Interchange. MayGoogle Scholar
  3. 3.
    Bailly-Bailleire E, Bengio S, Bimbot F, Hamouz M, Kittler J, Mariethoz J, Matas J, Messer K, Popovici V, Poree F, Ruiz B, Thiran J (2003) The BANCA database and evaluation protocol. Lect Notes Comput Sci 2688:625–638CrossRefGoogle Scholar
  4. 4.
    Chen S, Berglund E, Bigdeli A, Sanderson C, Lovell BC (2008) Experimental analysis of face recognition on still and CCTV images. Proceedings of the 2008 IEEE fifth international conference on advanced video and signal based surveillance, pp 317–324.Google Scholar
  5. 5.
    Chen X, Flynn PJ, Bowyer KW (2005) IR and visible light face recognition. Comput Vis Image Underst 99(3):332–358CrossRefGoogle Scholar
  6. 6.
    Delac K, Grgic M, Grgic S (2005) Independent comparative study of PCA, ICA, and LDA on the FERET data set. Int J Imaging Syst Technol 15(5):252–260CrossRefGoogle Scholar
  7. 7.
    Face recognition homepage, databases section:
  8. 8.
    Gao W, Cao B, Shan S, Zhou D, Zhang X, Zhao D (2004) The CAS-PEAL large-scale chinese face database and evaluation protocols. Technical Report No. JDL_TR_04_FR_001, Joint Research & Development Laboratory. CASGoogle Scholar
  9. 9.
    Gross R (2005) Face databases, in Li S and Jain A (eds.) Handbook of face recognition, SpringerGoogle Scholar
  10. 10.
    Keval HU, Sasse MA (2008) Can we ID from CCTV? Image quality in digital CCTV and face identification performance. Proc. SPIE 6982, paper ID 69820KGoogle Scholar
  11. 11.
    Klimpak B, Grgic M, Delac K (2006) Acquisition of a face database for video surveillance research. Proceedings of the 48th international symposium ELMAR-2006 focused on multimedia signal processing and communications, Zadar, Croatia, pp 111–114Google Scholar
  12. 12.
    Kong SG, Heo J, Abidi BR, Paik J, Abidi MA (2005) Recent advances in visual and infrared face recognition—a review. Comput Vis Image Underst 97(1):103–135CrossRefGoogle Scholar
  13. 13.
    Messer K, Matas J, Kittler J, Luettin J, Maitre G (1999) XM2VTSDB: The Extended M2VTS Database. Second int. conf. audio and vide-based biometric person authentication (AVBPA'99), Washington D.C., USA, pp 72–77Google Scholar
  14. 14.
    O'Toole AJ, Harms J, Snow SL, Hurst DR, Pappas MR, Ayyad JH, Abdi H (2005) A video database of moving faces and people. IEEE Trans. Pattern Analysis and Machine Intelligence 27(5):812–816CrossRefGoogle Scholar
  15. 15.
    Phillips PJ, Flynn PJ, Scruggs T, Bowyer KW, Chang J, Hoffman K, Marques J, Min J, Worek W (2005) Overview of the face recognition grand challenge. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) 1:947–954CrossRefGoogle Scholar
  16. 16.
    Phillips PJ, Moon H, Rizvi SA, Rauss P (2000) The FERET evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Analysis and Machine Intelligence 22(10):1090–1104CrossRefGoogle Scholar
  17. 17.
    Phillips PJ, Wechsler H, Huang J, Rauss P (1998) The FERET database and evaluation procedure for face recognition algorithms. Image Vis Comput 16(5):295–306CrossRefGoogle Scholar
  18. 18.
    Sim T, Baker S, Bsat M (2003) The CMU pose, illumination, and expression database. IEEE Trans. Pattern Analysis and Machine Intelligence 25(12):1615–1618CrossRefGoogle Scholar
  19. 19.
    Tan X, Chen S, Zhou Z, Zhang F (2006) Face recognition from a single image per person: a survey. Pattern Recogn 39(9):1725–1745zbMATHCrossRefGoogle Scholar
  20. 20.
    Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86CrossRefGoogle Scholar
  21. 21.
    Yambor W, Draper B, Beveridge JR (2002) Analyzing PCA-based face recognition algorithms: eigenvector selection and distance measures. In: Christensen H, Phillips J (eds) Empirical evaluation methods in computer vision. World Scientific, SingaporeGoogle Scholar
  22. 22.
    Zhao W, Chellappa R, Rosenfeld A, Phillips PJ (2003) Face recognition: a literature review. ACM Comput Surv 35(4):399–458CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.University of ZagrebZagrebCroatia

Personalised recommendations