Central regions | Technology & innovation

Deep learning algorithms for noninvasive age verification

12 Feb '19
An international research group led by Evgeny Bobrov of the Moscow Lomonosov State University (MSU) has come up with a new method of determining a persons age by studying the corners of his eyes. To do so, they developed an artificial intelligence system called PhotoAgeClock which is said to be able to tell ones age with an error of just two years.

The team wrote in their report that their focus was the development of aging biomarkers as the qualitative and quantitative indicators of the aging processes of the human body. Estimation of biological age is important for assessing the physiological state of an organism. The advent of machine learning lead to the development of the many age predictors commonly referred to as the aging clocks varying in biological relevance, ease of use, cost, actionability, interpretability, and applications.

The researchers have come up with a novel noninvasive class of visual photographic biomarkers of aging. They developed a simple and accurate predictor of chronological age using just the anonymized images of eye corners called the PhotoAgeClock. Deep neural networks were trained on 8,414 anonymized high-resolution images of eye corners labeled with the correct chronological age. For people within the age range of 20 to 80 in a specific population, the model was able to achieve a mean absolute error of 2.3 years and 95% Pearson and Spearman correlation, the team underscored.
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Locations: Moscow

Tags: eye (85) / age (558) / MSU (41) / artificial intelligence (22) / PhotoAgeClock (0) / Evgeny Bobrov (0) /

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