Technologies Used
A leading age verification company sought to enhance their age estimation system. Here's how Exometrics helped them achieve remarkable results.
As age verification becomes increasingly crucial in various industries, there's a growing demand for highly accurate age estimation systems. A prominent company in this field partnered with Exometrics to develop an advanced age estimation engine capable of reducing the Mean Absolute Error (MAE) of age estimates to 2.5 across all Fitzpatrick skin types.
The client required a solution that could handle diverse demographics and provide consistent results across different ethnicities and age groups. The system also needed to be fast and efficient for real-world applications.
Exometrics assembled a highly qualified team to address this challenge, comprising 3 Ph.D. holders (Applied Mathematics, Biostatistics, and Computer Science).
This expert group devised a multi-stage approach, developing three generations of age estimation engines. Our Data Scientists and Machine Learning specialists employed a diverse array of techniques, including ensemble models, deep learning architectures, and cutting-edge data preprocessing methodologies.
Mean Absolute Error
Our team developed and delivered a sophisticated age estimation system with state-of-the-art custom Machine Learning models
We achieved an MAE of 1.98 for the crucial 18-24 age group, outperforming a competing consulting firm
Our solution demonstrated consistency across various ethnicities, improving accuracy for underrepresented groups
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