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Deep Neural Networks for Face Recognition: Pairwise Optimisation

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Foundations of Intelligent Systems (ISMIS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11177))

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Abstract

Such factors as lighting conditions, head rotations and view angles affect the reliability of face recognition and make the recognition task difficult. Recognition of multiple subjects requires to learn class boundaries whose complexities quickly grow with the number of subjects. Artificial Neural Networks (ANNs) have provided efficient solutions, although their performances need to be improved. Multiclass and convolutional ANNs require massive computations and finding ad-hoc parameters in order to maximise the performance. Pairwise ANN structure has outperformed the multiclass ANNs on some face recognition tasks. We propose the pairwise optimisation for ANN, which requires a significantly smaller number of ad-hoc parameters and substantially fewer computations than the multiclass and convolutional networks.

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References

  1. Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 643–660 (2001)

    Article  Google Scholar 

  2. Hastie, T., Tibshirani, R.: Classification by pairwise coupling. Ann. Stat. 26(2), 451–471 (1998)

    Article  MathSciNet  Google Scholar 

  3. Jakaite, L., Schetinin, V.: Feature selection for Bayesian evaluation of trauma death risk. In: Katashev, A., Dekhtyar, Y., Spigulis, J. (eds.) 14th Nordic-Baltic Conference on Biomedical Engineering and Medical Physics. IFMBE Proceedings, vol. 20, pp. 123–126. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69367-3_33

    Google Scholar 

  4. Jakaite, L., Schetinin, V., Maple, C.: Bayesian assessment of newborn brain maturity from two-channel sleep electroencephalograms. Comput. Math. Methods Med. 1–7 (2012)

    Article  Google Scholar 

  5. Jakaite, L., Schetinin, V., Schult, J.: Feature extraction from electroencephalograms for Bayesian assessment of newborn brain maturity. In: 2011 24th International Symposium on Computer-Based Medical Systems (CBMS), pp. 1–6 (2011)

    Google Scholar 

  6. Kirby, M., Sirovich, L.: Application of the karhunen-loeve procedure for the characterization of human faces. IEEE Trans. Pattern Anal. Mach. Intell. 12(1), 103–108 (1990)

    Article  Google Scholar 

  7. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

  8. Moghaddam, B., Jebara, T., Pentland, A.: Bayesian face recognition. Pattern Recogn. 33(11), 1771–1782 (2000)

    Article  Google Scholar 

  9. Moon, H., Phillips, P.J.: Computational and performance aspects of PCA-based face-recognition algorithms. Perception 30(3), 303–321 (2001)

    Article  Google Scholar 

  10. Nebauer, C.: Evaluation of convolutional neural networks for visual recognition. IEEE Trans. Neural Netw. 9(4), 685–696 (1998)

    Article  Google Scholar 

  11. Nyah, N., Jakaite, L., Schetinin, V., Sant, P., Aggoun, A.: Learning polynomial neural networks of a near-optimal connectivity for detecting abnormal patterns in biometric data. In: 2016 SAI Computing Conference (SAI), pp. 409–413 (2016)

    Google Scholar 

  12. Phung, S.L., Bouzerdoum, A.: A pyramidal neural network for visual pattern recognition. IEEE Trans. Neural Netw. 18(2), 329–343 (2007)

    Article  Google Scholar 

  13. Price, D., Knerr, S., Personnaz, L., Dreyfus, G.: Pairwise neural network classifiers with probabilistic outputs. In: Neural Information Processing Systems, pp. 1109–1116 (1994)

    Google Scholar 

  14. Samaria, F.S., Harter, A.C.: Parameterisation of a stochastic model for human face identification. In: IEEE Workshop on Applications of Computer Vision, pp. 138–142 (1994)

    Google Scholar 

  15. Schetinin, V., et al.: Comparison of the Bayesian and randomized decision tree ensembles within an uncertainty envelope technique. J. Math. Model. Algorithms 5, 397–416 (2006)

    Article  MathSciNet  Google Scholar 

  16. Schetinin, V., Jakaite, L.: Classification of newborn EEG maturity with Bayesian averaging over decision trees. Expert Syst. Appl. 39(10), 9340–9347 (2012)

    Article  Google Scholar 

  17. Schetinin, V., Jakaite, L.: Extraction of features from sleep EEG for Bayesian assessment of brain development. PLOS ONE 12(3), 1–13 (2017)

    Article  Google Scholar 

  18. Schetinin, V., Jakaite, L., Krzanowski, W.J.: Prediction of survival probabilities with Bayesian decision trees. Expert Syst. Appl. 40(14), 5466–5476 (2013)

    Article  Google Scholar 

  19. Schetinin, V., Jakaite, L., Nyah, N., Novakovic, D., Krzanowski, W.: Feature extraction with GMDH-type neural networks for EEG-based person identification. Int. J. Neural Syst. (2018)

    Google Scholar 

  20. Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Networks 61, 85–117 (2015)

    Article  Google Scholar 

  21. Schönborn, S., Egger, B., Morel-Forster, A., Vetter, T.: Markov chain monte carlo for automated face image analysis. Int. J. Comput. Vis. 123(2), 160–183 (2017)

    Article  MathSciNet  Google Scholar 

  22. Soares, S., Antunes, C.H., Arajo, R.: Comparison of a genetic algorithm and simulated annealing for automatic neural network ensemble development. Neurocomputing 121, 498–511 (2013). Advances in Artificial Neural Networks

    Article  Google Scholar 

  23. Tivive, F., Bouzerdoum, A.: A face detection system using shunting inhibitory convolutional neural networks. In: IEEE International Joint Conference on Neural Networks, pp. 2571–2575 (2004)

    Google Scholar 

  24. Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 586–591 (1991)

    Google Scholar 

  25. Uglov, J., Jakaite, L., Schetinin, V., Maple, C.: Comparing robustness of pairwise and multiclass neural-network systems for face recognition. EURASIP J. Adv. Sign. Process. (2008)

    Google Scholar 

  26. Valenti, R., Sebe, N., Gevers, T., Cohen, I.: Machine learning techniques for face analysis. In: Cord, M., Cunningham, P. (eds.) Machine Learning Techniques for Multimedia. Cognitive Technologies, pp. 159–187. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-75171-7_7

  27. Yawichai, K., Kitjaidure, Y.: Multiview invariant shape recognition based on neural networks. In: 3rd IEEE Conference on Industrial Electronics and Applications, vol. 3, pp. 1538–1542 (2008)

    Google Scholar 

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Acknowledgements

The authors would like to thank Dr Livija Jakaite, a member of the supervisory team at the School of Computer Science of University of Bedfordshire, for useful and constructive comments.

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Authors and Affiliations

  1. School of Computer Science and Technology, University of Bedfordshire, Luton, UK

    Elitsa Popova, Athanasios Athanasopoulos, Efraim Ie, Nikolaos Christou & Ndifreke Nyah

Authors
  1. Elitsa Popova
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  2. Athanasios Athanasopoulos
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  3. Efraim Ie
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  4. Nikolaos Christou
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  5. Ndifreke Nyah
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Corresponding author

Correspondence to Ndifreke Nyah .

Editor information

Editors and Affiliations

  1. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci

  2. American University, Washington, DC, USA

    Nathalie Japkowicz

  3. Hong Kong Baptist University, Kowloon, Hong Kong

    Jiming Liu

  4. University of Cyprus, Nicosia, Cyprus

    George A. Papadopoulos

  5. University of North Carolina, Charlotte, NC, USA

    Zbigniew W. Raś

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Popova, E., Athanasopoulos, A., Ie, E., Christou, N., Nyah, N. (2018). Deep Neural Networks for Face Recognition: Pairwise Optimisation. In: Ceci, M., Japkowicz, N., Liu, J., Papadopoulos, G., Raś, Z. (eds) Foundations of Intelligent Systems. ISMIS 2018. Lecture Notes in Computer Science(), vol 11177. Springer, Cham. https://doi.org/10.1007/978-3-030-01851-1_10

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  • DOI: https://doi.org/10.1007/978-3-030-01851-1_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01850-4

  • Online ISBN: 978-3-030-01851-1

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