Adaptive Classification for Person Re-identification Driven by Change Detection

C. Pagano, E. Granger, R. Sabourin, G. L. Marcialis, F. Roli


Person re-identification from facial captures remains a challenging problem in video surveillance, in large part due to variations in capture conditions over time. The facial model of a target individual is typically designed during an enrolment phase, using a limited number of reference samples, and may be adapted as new reference videos become available. However incremental learning of classifiers in changing capture conditions may lead to knowledge corruption. This paper presents an active framework for an adaptive multi-classifier system for video-to-video face recognition in changing surveillance environments. To estimate a facial model during the enrolment of an individual, facial captures extracted from a reference video are employed to train an individual-specific incremental classifier. To sustain a high level of performance over time, a facial model is adapted in response to new reference videos according the type of concept change. If the system detects that the facial captures of an individual incorporate a gradual pattern of change, the corresponding classifier(s) are adapted through incremental learning. In contrast, to avoid knowledge corruption, if an abrupt pattern of change is detected, a new classifier is trained on the new video data, and combined with the individual’s previously-trained classifiers. For validation, a specific implementation is proposed, with ARTMAP classifiers updated using an incremental learning strategy based on Particle Swarm Optimization, and the Hellinger Drift Detection Method is used for change detection. Simulation results produced with Faces in Action video data indicate that the proposed system allows for scalable architectures that maintains a significantly higher level of accuracy over time than a reference passive system and an adaptive Transduction Confidence Machine-kNN classifier, while controlling computational complexity.


  1. Ahonen, T. (2006). Face description with local binary patterns: Application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12):2037-2041.
  2. Alippi, C., Boracchi, G., and Roveri, M. (2013). Just-intime classifiers for recurrent concepts. IEEE Transactions on Neural Networks and Learning Systems, 24(4):620-634.
  3. Barry, M. and Granger, E. (2007). Face recognition in video using a what-and-where fusion neural network. In Neural Networks, 2007. IJCNN 2007. International Joint Conference on, pages 2256-2261.
  4. C. Pagano, E. Granger, R. Sabourin, D. G. (2012). Detector ensembles for face recognition in video surveillance. In Neural Networks (IJCNN), The 2012 International Joint Conference on, pages 1-8.
  5. Carpenter, G. A., Grossberg, S., Markuzon, N., Reynolds, J. H., Rosen, D. B., and Member, S. (1992). Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Transactions on Neural Networks, 3(5):698-713.
  6. Connolly, J.-F., Granger, E., and Sabourin, R. (2012). An adaptive classification system for video-based face recognition. Information Sciences, 192:50-70.
  7. Ditzler, G. and Polikar, R. (2011). Hellinger distance based drift detection for nonstationary environments. In Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), 2011 IEEE Symposium on, pages 41-48.
  8. Eberhart, R. C. and Kennedy, J. (1995). A new optimizer using particle swarm theory. In Proceedings of the sixth international symposium on micro machine and human science, volume 1, pages 39-43. New York, NY.
  9. Fritzke, B. (1996). Growing self-organizing networks - why? In In ESANN96: European Symposium on Artificial Neural Networks, pages 61-72. Publishers.
  10. Goh, R., Liu, L., Liu, X., and Chen, T. (2005). The CMU face in action (FIA) database. In Analysis and Modelling of Faces and Gestures, pages 255-263.
  11. Gorodnichy, D. (2005). Video-based framework for face recognition in video. In Proceedings Canadian Conference on Computer and Robot Vision, pages 330- 338.
  12. Hart, P. (1968). The condensed nearest neighbor rule. IEEE Transactions on Information Theory, 14(3):515-516.
  13. Kittler, J. and Alkoot, F. M. (2003). Sum versus vote fusion in multiple classifier systems. In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 25, pages 110-115.
  14. Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience.
  15. Kuncheva, L. I. (2008). Classifier ensembles for detecting concept change in streaming data: Overview and perspectives. In 2nd Workshop SUEMA 2008 (ECAI 2008), pages 5-10.
  16. Li, F. and Wechsler, H. (2005). Open set face recognition using transduction. IEEE Trans. Pattern Anal. Mach. Intell., 27(11):1686-1697.
  17. Lim, C. and Harrison, R. (1995). Probabilistic fuzzy ARTMAP: an autonomous neural network architecture for bayesian probability estimation. In Proceedings of 4th International Conference on Artificial Neural Networks, pages 148-153.
  18. Matta, F. and Dugelay, J.-L. (2009). Person recognition using facial video information: A state of the art. Journal of Visual Languages & Computing, 20(3):180 - 187.
  19. Minku, L., White, A., and Yao, X. (2010). The impact of diversity on online ensemble learning in the presence of concept drift. EEE Transactions on Knowledge and Data Engineering, 22(5):730-742.
  20. Minku, L. L. and Yao, X. (2012). DDD: A New Ensemble Approach for Dealing with Concept Drift. IEEE Transactions on Knowledge and Data Engineering, 24(4):619-633.
  21. Narasimhamurthy, A. and Kuncheva, L. (2007). A framework for generating data to simulate changing environments. In 25th IASTED International MultiConference: artificial intelligence and application, pages 384-389.
  22. Nickabadi, A., Ebadzadeh, M. M., and Safabakhsh, R. (2008). DNPSO: A dynamic niching particle swarm optimizer for multi-modal optimization. In 2008 IEEE Congress on Evolutionary Computation, CEC 2008, pages 26-32.
  23. Oh, I.-S. and Suen, C. Y. (2002). A class-modular feedforward neural network for handwriting recognition. Pattern Recognition, 35(1):229 - 244. Shape representation and similarity for image databases.
  24. Polikar, R. and Upda, L. (2001). Learn++ : An Incremental Learning Algorithm for supervised neural networks. In IEEE Transactions on Systems, Man and Cybernetics, volume 31, pages 497-508.
  25. Tax, D. and Duin, R. (2008). Growing a multi-class classifier with a reject option. Pattern Recognition Letters, 29:1565-1570.
  26. Viola, P. and Jones, M. J. (2004). Robust real-time face detection. International Journal of Computer Vision, 57:137-154.
  27. Zhou, S. K., Chellappa, R., and Zhao, W. (2006). Unconstrained face recognition, volume 5. Springer.

Paper Citation

in Harvard Style

Pagano C., Granger E., Sabourin R., Marcialis G. and Roli F. (2015). Adaptive Classification for Person Re-identification Driven by Change Detection . In Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-076-5, pages 45-55. DOI: 10.5220/0005184700450055

in Bibtex Style

author={C. Pagano and E. Granger and R. Sabourin and G. L. Marcialis and F. Roli},
title={Adaptive Classification for Person Re-identification Driven by Change Detection},
booktitle={Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},

in EndNote Style

JO - Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Adaptive Classification for Person Re-identification Driven by Change Detection
SN - 978-989-758-076-5
AU - Pagano C.
AU - Granger E.
AU - Sabourin R.
AU - Marcialis G.
AU - Roli F.
PY - 2015
SP - 45
EP - 55
DO - 10.5220/0005184700450055