All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

Learning Robust Features for Gait Recognition by Maximum Margin Criterion

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F16%3A00090367" target="_blank" >RIV/00216224:14330/16:00090367 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1109/ICPR.2016.7899750" target="_blank" >https://doi.org/10.1109/ICPR.2016.7899750</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/ICPR.2016.7899750" target="_blank" >10.1109/ICPR.2016.7899750</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Learning Robust Features for Gait Recognition by Maximum Margin Criterion

  • Original language description

    In the field of gait recognition from motion capture data, designing human-interpretable gait features is a common practice of many fellow researchers. To refrain from ad-hoc schemes and to find maximally discriminative features we may need to explore beyond the limits of human interpretability. This paper contributes to the state-of-the-art with a machine learning approach for extracting robust gait features directly from raw joint coordinates. The features are learned by a modification of Linear Discriminant Analysis with Maximum Margin Criterion so that the identities are maximally separated and, in combination with an appropriate classifier, used for gait recognition. Experiments on the CMU MoCap database show that this method outperforms eight other relevant methods in terms of the distribution of biometric templates in respective feature spaces expressed in four class separability coefficients.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    IN - Informatics

  • OECD FORD branch

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2016

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Proceedings of the 23rd IEEE/IAPR International Conference on Pattern Recognition (ICPR 2016)

  • ISBN

    9781509048472

  • ISSN

  • e-ISSN

  • Number of pages

    6

  • Pages from-to

    901-906

  • Publisher name

    IEEE

  • Place of publication

    USA

  • Event location

    Cancun, Mexico

  • Event date

    Jan 1, 2016

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article