Publication detail

Learning of a Robusted Nearest Neighbor Classifier Using Multiple Training Data

MALACH, T. POMĚNKOVÁ, J.

Original Title

Learning of a Robusted Nearest Neighbor Classifier Using Multiple Training Data

Type

conference paper

Language

English

Original Abstract

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.

Keywords

Template creation; Nearest neighbor; Multiple training data; Surveillance face recognition.

Authors

MALACH, T.; POMĚNKOVÁ, J.

Released

25. 5. 2016

Location

Bratislava

ISBN

978-1-4673-9554-0

Book

Proceedings The 23rd International Conference on Systems, Signals and Image Processing

Pages from

47

Pages to

50

Pages count

4

URL

https://ieeexplore.ieee.org/document/7502701

BibTex

@inproceedings{BUT127619,
  author="Tobiáš {Malach} and Jitka {Dluhá}",
  title="Learning of a Robusted Nearest Neighbor Classifier
Using Multiple Training Data",
  booktitle="Proceedings The 23rd International Conference on Systems, Signals and Image Processing",
  year="2016",
  pages="47--50",
  address="Bratislava",
  doi="10.1109/IWSSIP.2016.7502701",
  isbn="978-1-4673-9554-0",
  url="https://ieeexplore.ieee.org/document/7502701"
}