Consecutive gender and age classification from facial images based on ranked local binary patterns
Автор: Rybintsev Andrey Vladimirovich, Konushin Vadim Sergeyevich, Konushin Anton Sergeyevich
Журнал: Компьютерная оптика @computer-optics
Рубрика: Обработка изображений: Восстановление изображений, выявление признаков, распознавание образов
Статья в выпуске: 5 т.39, 2015 года.
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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.
Machine learning, image classification, local binary patterns, adaboost, support vector machine, bootstrapping, support vector regression, gender classification, age classification
Короткий адрес: https://sciup.org/14059421
IDR: 14059421 | DOI: 10.18287/0134-2452-2015-39-5-762-769
