Consecutive gender and age classification from  facial images based on ranked local binary patterns
A.V. Rybintsev, V.S. Konushin, A.S. Konushin

 

M.V. Lomonosov Moscow State University, Moscow, Russia,
Video Analysis Technologies LLC, Moscow, Russia,

Higher School of Economics, Moscow, Russia

Full text of article: Russian language.

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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.

Citation:
Rybintsev AV, Konushin VS, Konushin AS. Consecutive gender and age classification from facial images based on ranked local binary patterns. Computer Optics 2015; 39(5): 762-9. – DOI: 10.18287/0134-2452-2015-39-5-762-769.

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