ITL Boasts of Highest Accuracy in Age Estimation After ACCS Study

Innovative Technology Ltd. (ITL) has renewed its certification from the Age Check Certification Scheme (ACCS), noting significant improvements in its biometric age verification technology since its last review in 2021.

The company’s ICU Lite solution is designed to regulate access to age-restricted products and venues across retail and gaming sectors, and has not only maintained its Challenge 25 recommendation but has also set a new record.

The ACCS, a UKAS-accredited body, plays an important role in certifying systems that accurately check age and identity, thereby protecting minors from age-sensitive products and services. The United Kingdom Accreditation Service (UKAS) is the sole national accreditation body recognized by the British government to assess organizations that provide certification, testing, inspection, and calibration services. Its accreditation of the Age Check Certification Scheme enables ACCS to perform independent and authoritative certification of systems that verify age and identity, ensuring these systems meet established standards of accuracy and reliability.

“Our mission is to protect children from the harm associated with access to age-restricted products, content and services,” explained ACCS CEO Tony Allen. “This report concluded that ICU Lite is suitably accurate and fit for deployment in a Challenge 25 policy area, achieving the highest rate of accuracy.”

According to Dr. Andrew O’Brien, the Biometrics Product Manager at ITL, his firm’s ICU Lite system now boasts a Mean Absolute Error (MAE) of less than one year, an accuracy unmatched by other systems, making ITL a leader in age estimation technology. “The findings highlight that on average, our technology over estimates by only 0.39 years, with a MAE of 0.94 years,” he asserted.

Mean Absolute Error is a statistical measure used to assess the accuracy of predictions made by a model. It calculates the average magnitude of errors in a set of predictions, without considering their direction (i.e., over- or underestimating), thus providing a clear measure of prediction accuracy in numerical terms.

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May 9, 2024 – by Alex Perala