Multimedia Tools and Applications

, Volume 73, Issue 1, pp 109–128 | Cite as

ES-RU: an entropy based rule to select representative templates in face surveillance

  • Maria De Marsico
  • Michele Nappi
  • Daniel RiccioEmail author


ES-RU is a system for video sequence indexing. Video frames are annotated according to the identities of appearing subjects. The system architecture is designed by distributing the different processing steps across dedicated modules. These modules interact with each other to accomplish the final task. Such modularity is also designed to allow a high system flexibility, because it is possible to independently substitute each component with a different one performing the same task using a different method. As an example, face detection is presently performed by Viola–Jones algorithm, but the corresponding module might be substituted by one exploiting neural networks or support vector machines (which are actually more computationally demanding). In detail, ES-RU implements both face location and analysis, and an algorithm to select the most representative templates for the selected identities. The novelty of the algorithm for template analysis and selection relies on the proposed use of the concept of entropy. This concept is the base of most techniques that exploit relative entropy to estimate the degree of uniqueness which is assured by a biometric trait, when processed by a Feature Extraction Technique (FET). In this paper, entropy is introduced as a tool to evaluate the contribution of each sample in guaranteeing a suitable diversification of the templates that make up the gallery of a relevant subject. Video-surveillance activities cause to gather a huge amount of templates to be used for tracking and re-identifying subjects. However, most of these templates are not informative enough to be useful. The aim of our approach is to provide an effective technique to keep only the most “representative” of them, i.e. those that provide a sufficient level of diversification. This allows faster processing (less comparisons) and better results (it is possible to recognize a subject under different conditions). ES-RU was tested on six video clips and on a subset of the SCFace database to assess its performances.


Biometrics Video surveillance Face indexing Entropy 


  1. 1.
    Abate AF, Nappi M, Riccio D, Sabatino G (2007) 2D and 3D face recognition: a survey. Pattern Recognit Lett 28(14):1885–1906CrossRefGoogle Scholar
  2. 2.
    Adler A, Youmaran R, Loyka S (2006) Towards a measure of biometric information. In: Proceedings of the Canadian Conference on Electrical and Computer Engineering, pp 210–213Google Scholar
  3. 3.
    Becker B, Ortiz E (2009) Evaluation of face recognition techniques for application to facebook. IEEE International Conf. on Automatic Face & Gesture Recognition, DecemberGoogle Scholar
  4. 4.
    Bhatnagar J, Kumar A (2009) On estimating some performance indices for biometric identification. Pattern Recognit 42(5):1805–1818Google Scholar
  5. 5.
    Bhatnagar J, Kumar A, Saggar N (2007) A novel approach to improve biometric recognition using rank level fusion. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–6Google Scholar
  6. 6.
    Bhatnagar J, Lall B, Patney RK (2010) Performance issues in biometric authentication based on information theoretic concepts: a review. IETE Tech Rev 27:273–285CrossRefGoogle Scholar
  7. 7.
    Choi JY, De Neve W, Ro YM (2010) Towards an automatic face indexing system for actor-based video services in an IPTV environment. IEEE Trans Consum Electron 56(1):147–155CrossRefGoogle Scholar
  8. 8.
    Cover TM, Thomas JA (1991) Elements of information theory. Wiley, New YorkCrossRefzbMATHGoogle Scholar
  9. 9.
    De Marsico M, Nappi M, Riccio D (2010) FACE: Face analysis for commercial entities. In: Proceedings of the international conference on image processing, pp 1597–1600Google Scholar
  10. 10.
    De Marsico M, Nappi M, Riccio D (2011) Measuring measures for face sample quality. In: Proceedings of the international ACM workshop on multimedia in forensics and intelligence (MiFor’11)Google Scholar
  11. 11.
    Doretto G, Sebastian T, Tu P, Rittscher J (2011) Appearance-based person re-identification in camera networks: problem overview and current approaches. J Ambient Intell Hum Comput 1–25Google Scholar
  12. 12.
    Fischer M, Kemal Ekenel H, Stiefelhagen R (2011) Person re-identification in TV series using robust face recognition and user feedback. Multimed Tools Appl 55(1):83–104CrossRefGoogle Scholar
  13. 13.
    Gallager RG (1968) Information theory and reliable communication. John Wiley & SonsGoogle Scholar
  14. 14.
    Golić JD, Baltatu M (2008) entropy analysis and new constructions of biometric key generation systems. In: IEEE transactions on information theory, vol. 54, no. 5, pp 2026–2040Google Scholar
  15. 15.
    Grgic M, Delac K, Grgic S (2009) SCface-surveillance cameras face database. Multimed Tools Appl J 51(3):863–879CrossRefGoogle Scholar
  16. 16.
    Jain AK, Nandakumar K, Nagar A (2008) Biometric template security, EURASIP Journal on Advances in Signal Processing, Special Issue on Pattern Recognition Methods for BiometricsGoogle Scholar
  17. 17.
    Jassim AJ, Al-Assam H, Abbound AJ, Sellahewa H (2010) Analysis of relative entropy, accuracy, and quality of face biometric. In: Proceedings of the workshop on pattern recognition for IT, Darmstadt, Germany, SeptemberGoogle Scholar
  18. 18.
    Maggio E, Piccardo E, Regazzoni C, Cavallaro A (2007) “Particle PHD filter for multi-target visual tracking”. In: Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007), Honolulu (USA), April 15–20Google Scholar
  19. 19.
    Martinel N, Foresti GL (2012) Multi-signature based person re-identification. Electron Lett 48(13):765–767CrossRefGoogle Scholar
  20. 20.
    Milborrow S, Nicolls F (2008) Locating facial features with an extended active shape model. Eur Conf Comp Vis 504–513Google Scholar
  21. 21.
    Schmid NA, O’Sullivan JA (2004) Performance prediction methodology for biometric system using large deviations approach. In: IEEE transactions on signal processing: supplement on secure media, vol. 52, no. 10, pp 3036–3045Google Scholar
  22. 22.
    Torres L, Vilà J (2002) Automatic face recognition for video indexing applications. Pattern Recognit 35(3):615–625CrossRefzbMATHGoogle Scholar
  23. 23.
    Viola P, Jones M (2004) Robust real-time face detection. Int J Comput Vis 57(2):137–154CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Maria De Marsico
    • 1
  • Michele Nappi
    • 2
  • Daniel Riccio
    • 2
    Email author
  1. 1.Sapienza University of RomeRomeItaly
  2. 2.University of SalernoFiscianoItaly

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