A connectionist computational method for face recognition

Francisco A. Pujol; Higinio Mora; José A. Girona-Selva

International Journal of Applied Mathematics and Computer Science (2016)

  • Volume: 26, Issue: 2, page 451-465
  • ISSN: 1641-876X

Abstract

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In this work, a modified version of the elastic bunch graph matching (EBGM) algorithm for face recognition is introduced. First, faces are detected by using a fuzzy skin detector based on the RGB color space. Then, the fiducial points for the facial graph are extracted automatically by adjusting a grid of points to the result of an edge detector. After that, the position of the nodes, their relation with their neighbors and their Gabor jets are calculated in order to obtain the feature vector defining each face. A self-organizing map (SOM) framework is shown afterwards. Thus, the calculation of the winning neuron and the recognition process are performed by using a similarity function that takes into account both the geometric and texture information of the facial graph. The set of experiments carried out for our SOM-EBGM method shows the accuracy of our proposal when compared with other state-of the-art methods.

How to cite

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Francisco A. Pujol, Higinio Mora, and José A. Girona-Selva. "A connectionist computational method for face recognition." International Journal of Applied Mathematics and Computer Science 26.2 (2016): 451-465. <http://eudml.org/doc/280119>.

@article{FranciscoA2016,
abstract = {In this work, a modified version of the elastic bunch graph matching (EBGM) algorithm for face recognition is introduced. First, faces are detected by using a fuzzy skin detector based on the RGB color space. Then, the fiducial points for the facial graph are extracted automatically by adjusting a grid of points to the result of an edge detector. After that, the position of the nodes, their relation with their neighbors and their Gabor jets are calculated in order to obtain the feature vector defining each face. A self-organizing map (SOM) framework is shown afterwards. Thus, the calculation of the winning neuron and the recognition process are performed by using a similarity function that takes into account both the geometric and texture information of the facial graph. The set of experiments carried out for our SOM-EBGM method shows the accuracy of our proposal when compared with other state-of the-art methods.},
author = {Francisco A. Pujol, Higinio Mora, José A. Girona-Selva},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {pattern recognition; face recognition; neural networks; self-organizing maps},
language = {eng},
number = {2},
pages = {451-465},
title = {A connectionist computational method for face recognition},
url = {http://eudml.org/doc/280119},
volume = {26},
year = {2016},
}

TY - JOUR
AU - Francisco A. Pujol
AU - Higinio Mora
AU - José A. Girona-Selva
TI - A connectionist computational method for face recognition
JO - International Journal of Applied Mathematics and Computer Science
PY - 2016
VL - 26
IS - 2
SP - 451
EP - 465
AB - In this work, a modified version of the elastic bunch graph matching (EBGM) algorithm for face recognition is introduced. First, faces are detected by using a fuzzy skin detector based on the RGB color space. Then, the fiducial points for the facial graph are extracted automatically by adjusting a grid of points to the result of an edge detector. After that, the position of the nodes, their relation with their neighbors and their Gabor jets are calculated in order to obtain the feature vector defining each face. A self-organizing map (SOM) framework is shown afterwards. Thus, the calculation of the winning neuron and the recognition process are performed by using a similarity function that takes into account both the geometric and texture information of the facial graph. The set of experiments carried out for our SOM-EBGM method shows the accuracy of our proposal when compared with other state-of the-art methods.
LA - eng
KW - pattern recognition; face recognition; neural networks; self-organizing maps
UR - http://eudml.org/doc/280119
ER -

References

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