(Press-News.org)
Tallinn, Estonia – 7th May 2024, 10 AM CET – Haut.AI, a leader in responsible skincare artificial intelligence (AI) development, today announced a breakthrough research paper demonstrating the effectiveness of using hand images for accurate age prediction. This innovative approach offers a viable alternative to traditional facial photo methods and promotes fairer AI solutions.
The study, titled “Predicting human chronological age via AI analysis of dorsal hand versus facial images: A study in a cohort of Indian females,” shows that AI models trained on hand images achieve comparable accuracy to those using facial images, with an average error of 4.1 and 4.7 years in predicting chronological age. This research is particularly significant for ethnic skin, as it was trained using the Indian population dataset and represents the first AI model for age prediction specifically designed with a diverse dataset that includes a wide range of skin tones.
“Our research demonstrates that your age can be determined just as accurately from a picture of your hands as from your face,” said Anastasia Georgievskaya, the CEO of Haut.AI. “This not only opens doors for new applications of AI technology but also has the potential to mitigate biases often associated with conventional systems. This aligns perfectly with our commitment to developing fair and responsible AI solutions.”
By using hand images, Haut.AI aims to address potential biases that can arise from facial recognition systems due to factors like ethnicity and facial features. The research emphasizes the importance of utilizing diverse datasets in AI development to ensure unbiased and inclusive solutions. This technology offers an alternative for situations where facial images are unavailable or less preferred.
Understanding the Aging Process:
The study goes beyond just the accuracy of age prediction. By analyzing how specific features on hands and faces influence the model’s predictions, the research contributes to a better understanding of the aging process. Researchers found that areas around the eyes, nose, mouth, and forehead were important for facial age prediction by AI. These areas often show wrinkles, sagging, and other signs of aging. In hands, features like wrinkles, knuckles, and bone prominence were significant for age prediction. Antiaging interventions that address these features will make you look younger to neural networks and, most likely, to humans, too.
Haut.AI is committed to responsible AI development and fostering a future where technology benefits everyone. This research is a significant step forward in achieving these goals.
About HAUT.AI
Positioned as a leading European AI company, Haut.AI specializes in the delivery of highly personalized skincare and beauty experiences. Backed by world-class research, science, and technology teams, Haut.AI’s Software-as-a-Service (SaaS) platform was trained on an extensive dataset of three million image data points, allowing it to assess over 150 distinct multidimensional face biomarkers and enabling the delivery of interactive, customized aesthetic recommendations—with just the quick snap of a selfie!
Already embraced by leading names in the beauty, skincare, and pharmaceutical industries, Haut.AI’s platform is utilized by global brands such as Beiersdorf, Ulta Beauty, the largest beauty retailer in the United States, Almirall Pharma, Phoenix, and Dr Max Pharma.
As a proudly women-led company, Haut.AI is on a mission to establish personalized skincare as the new standard for beauty and wellness brands worldwide, all powered by the capabilities of artificial intelligence. To delve deeper into Haut.AI’s innovative approach, visit the website: www.haut.ai.
Media Contact
Anastasia Georgievskaya
CEO at Haut.AI
nastya@haut.ai
Article:
Georgievskaya, A., Tlyachev, T., Kiselev, K., Hillebrand, G., Chekanov, K., Danko, D., Golodyaev, A., & Majmudar, G. (2024). Predicting human chronological age via AI analysis of dorsal hand versus facial images: A study in a cohort of Indian females. Experimental dermatology, 33(3), e15045. https://doi.org/10.1111/exd.15045
https://doi.org/10.1111/exd.15045
END
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