Face Class Modeling based on Local Appearance for Recognition

Mokhtar Taffar, Serge Miguet

2017

Abstract

This work proposes a new formulation of the objects modeling combining geometry and appearance. The object local appearance location is referenced with respect to an invariant which is a geometric landmark. The appearance (shape and texture) is a combination of Harris-Laplace descriptor and local binary pattern (LBP), all is described by the invariant local appearance model (ILAM). We applied the model to describe and learn facial appearances and to recognize them. Given the extracted visual traits from a test image, ILAM model is performed to predict the most similar features to the facial appearance, first, by estimating the highest facial probability, then in terms of LBP Histogram-based measure. Finally, by a geometric computing the invariant allows to locate appearance in the image. We evaluate the model by testing it on different images databases. The experiments show that the model results in high accuracy of detection and provides an acceptable tolerance to the appearance variability.

Paper Citation

in Harvard Style

Taffar M. and Miguet S. (2017). Face Class Modeling based on Local Appearance for Recognition . In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 128-137. DOI: 10.5220/0006185201280137

in Bibtex Style

@conference{icpram17,
author={Mokhtar Taffar and Serge Miguet},
title={Face Class Modeling based on Local Appearance for Recognition},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={128-137},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006185201280137},
isbn={978-989-758-222-6},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Face Class Modeling based on Local Appearance for Recognition
SN - 978-989-758-222-6
AU - Taffar M.
AU - Miguet S.
PY - 2017
SP - 128
EP - 137
DO - 10.5220/0006185201280137