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Computer Optics, 2015, Volume 39, Issue 5, Pages 762–769 (Mi co42)  

IMAGE PROCESSING, PATTERN RECOGNITION

Consecutive gender and age classification from facial images based on ranked local binary patterns

A. V. Rybintseva, V. S.  Konushinb, A. S.  Konushinca

a M.V. Lomonosov Moscow State University, Moscow, Russia
b Video Analysis Technologies LLC, Moscow, Russia
c Higher School of Economics, Moscow, Russia

Abstract: A new algorithm for consecutive classification of gender and age based on a two-stage support vector regression is proposed. Only most significant local binary patterns are used to describe the image. To enhance the gender classification accuracy we use bootstrapping with the training based on difficult examples, whereas the age classification is improved through the use of floating age ranges.

Keywords: machine learning, image classification, gender classification, age classification, local binary patterns, Adaboost, support vector machine, bootstrapping, support vector regression.

Funding Agency Grant Number
Russian Foundation for Basic Research 14-01-00849
This work was supported by grant RFFI 14-01-00849.


DOI: https://doi.org/10.18287/0134-2452-2015-39-5-762-769

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Full text: http://www.computeroptics.smr.ru/.../390517.html
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Received: 13.08.2015
Revised: 11.11.2015

Citation: A. V. Rybintsev, V. S. Konushin, A. S. Konushin, “Consecutive gender and age classification from facial images based on ranked local binary patterns”, Computer Optics, 39:5 (2015), 762–769

Citation in format AMSBIB
\Bibitem{RybKonKon15}
\by A.~V.~Rybintsev, V.~S.~ Konushin, A.~S.~ Konushin
\paper Consecutive gender and age classification from facial images based on ranked local binary patterns
\jour Computer Optics
\yr 2015
\vol 39
\issue 5
\pages 762--769
\mathnet{http://mi.mathnet.ru/co42}
\crossref{https://doi.org/10.18287/0134-2452-2015-39-5-762-769}


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