Detail publikace

Learning of a Robusted Nearest Neighbor Classifier Using Multiple Training Data

Originální název

Learning of a Robusted Nearest Neighbor Classifier Using Multiple Training Data

Anglický název

Learning of a Robusted Nearest Neighbor Classifier Using Multiple Training Data

Jazyk

en

Originální abstrakt

This paper deals with the application of face recognition in surveillance CCTV systems and effective usage of so called recognition clues. These clues are enrollment of multiple training face images and their usages in classifier training and real-time management of template database. A survey on classifiers from perspective of practical application is given resulting in the defense of nearest neighbor based classifiers. They are competitive with state of the art classifiers and are suitable for practical application. Template creation using multiple training face images and enhancement of NN-based classifier performance is achieved by novel approach. It consist of quantile interval method for template creation and robusted NNbased classifier using spatial templates with soft boundaries. We evaluate proposed recognition framework on highly representative IFaViD dataset. Proposed framework outperforms state of the art approaches.

Anglický abstrakt

This paper deals with the application of face recognition in surveillance CCTV systems and effective usage of so called recognition clues. These clues are enrollment of multiple training face images and their usages in classifier training and real-time management of template database. A survey on classifiers from perspective of practical application is given resulting in the defense of nearest neighbor based classifiers. They are competitive with state of the art classifiers and are suitable for practical application. Template creation using multiple training face images and enhancement of NN-based classifier performance is achieved by novel approach. It consist of quantile interval method for template creation and robusted NNbased classifier using spatial templates with soft boundaries. We evaluate proposed recognition framework on highly representative IFaViD dataset. Proposed framework outperforms state of the art approaches.

BibTex


@inproceedings{BUT127619,
  author="Tobiáš {Malach} and Jitka {Poměnková}",
  title="Learning of a Robusted Nearest Neighbor Classifier
Using Multiple Training Data",
  annote="This paper deals with the application of face
recognition in surveillance CCTV systems and effective usage of so called recognition clues. These clues are enrollment of multiple training face images and their usages in classifier training and real-time management of template database. A survey on classifiers from perspective of practical application is given resulting in the defense of nearest neighbor based classifiers. They are competitive with state of the art classifiers and are
suitable for practical application. Template creation using multiple training face images and enhancement of NN-based classifier performance is achieved by novel approach. It consist of quantile interval method for template creation and robusted NNbased classifier using spatial templates with soft boundaries. We evaluate proposed recognition framework on highly representative IFaViD dataset. Proposed framework outperforms
state of the art approaches.",
  booktitle="Proceedings The 23rd International Conference on Systems, Signals and Image Processing",
  chapter="127619",
  howpublished="online",
  year="2016",
  month="may",
  pages="47--50",
  type="conference paper"
}