This neural network can detect age, gender of people in videos with more accuracy
Due to various conditions of observations or even slight head rotation, prediction of the same person’s age in different video frames varies in the range of 5 years, plus or minus.
A team of researchers has trained neural networks to identify certain people on video, detecting their age and gender more quickly -- almost 20% more accurately.
The development has already become the basis for offline detection systems in Android mobile apps, said researchers from National Research University's Higher School of Economics.
Modern neural networks detect gender on videos with a 90% accuracy and the situation with age prediction is much more complicated.
Due to various conditions of observations or even slight head rotation, prediction of the same person's age in different video frames varies in the range of 5 years, plus or minus.
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Experts in computer vision headed by Professor Andrey Savchenko found a way to optimise neural networks' operations.
Experiments on several video datasets proved that their technology allows for implementation of today's most accurate algorithms of gender and age recognition on video as compared to other popular convolutional neural networks, said the study published in an article titled "Video-based age and gender recognition in mobile applications".
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The findings may be used by the smartphone manufacturer to create various recommendation systems.
For example, if a user has a considerable amount of content with a toddler, he or she would be offered an advertisement for a children's store.
"If they have a lot of friends in photos taken on certain days, the smartphone will suggest a restaurant for a party. This technology has already attracted interest of the biggest smartphone manufacturer," said the study.
"To avoid wasting time and battery charge, we use our efficient convolutional neural network to analyse the images," said Savchenko, adding that "we also pay a lot of attention to privacy: processing is done only on the user's smartphone in offline mode".
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