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Local Texture Pattern Selection for Efficient Face Recognition and Tracking

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 403)

Abstract

This paper describes the research aimed at finding the optimal configuration of the face recognition algorithm based on local texture descriptors (binary and ternary patterns). Since the identification module was supposed to be a part of the face tracking system developed for interactive wearable computer, proper feature selection, allowing for real-time operation, became particularly important. Our experiments showed that it is unfeasible to reduce the computational complexity of the process by choosing discriminant regions of interest on the basis of the training set. The application of simulated annealing, however, to the selection of the most discriminant LTP codes provided satisfactory results.

Keywords

  • Face recognition
  • Feature selection
  • Local binary patterns

This work was funded in part by NCBiR, FWF, SNSF, ANR, and FNR in the framework of the ERA-NET CHIST-ERA II, project eGlasses—The interactive eyeglasses for mobile, perceptual computing, and by statutory funds of the Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology.

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Correspondence to Maciej Smiatacz .

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Smiatacz, M., Rumiński, J. (2016). Local Texture Pattern Selection for Efficient Face Recognition and Tracking. In: Burduk, R., Jackowski, K., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds) Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. Advances in Intelligent Systems and Computing, vol 403. Springer, Cham. https://doi.org/10.1007/978-3-319-26227-7_34

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  • DOI: https://doi.org/10.1007/978-3-319-26227-7_34

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  • Online ISBN: 978-3-319-26227-7

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