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New Ordering-Based Pruning Metrics for Ensembles of Classifiers in Imbalanced Datasets Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Local Texture Pattern Selection for Efficient Face Recognition and Tracking

Authors : Maciej Smiatacz, Jacek Rumiński

Published in: Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015

Publisher: Springer International Publishing

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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.

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Metadata
Title
Local Texture Pattern Selection for Efficient Face Recognition and Tracking
Authors
Maciej Smiatacz
Jacek Rumiński
Copyright Year
2016
Book
Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015

Print ISBN: 978-3-319-26225-3

Electronic ISBN: 978-3-319-26227-7

Copyright Year: 2016

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

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