Chinese authorities actively recruit test subjects for cutting-edge facial recognition experiments amid shifts in the Uyghur population’s movements and exchanges. Participants receive substantial compensation, with top payouts reaching 70,000 yuan—equivalent to approximately 1.5 million Korean won—for stays lasting up to 60 days.
Program Launch and Objectives
On May 9, officials at the Uyghur Joint Research and Training Center (ACC) publicly announced recruitment for the “Ji Gu Byeol-3” facial recognition safety verification program. The initiative targets women undergoing re-education or population adjustments, aiming to analyze physical changes over extended periods in controlled environments.
Target Demographics
Ideal candidates are Uyghur women aged 30 to 55, standing between 160 and 175 cm tall. Authorities prioritize individuals with almond-shaped eyes, high cheekbones, prominent Mongolian features, and other distinctive traits common in the region. No formal education is required.
Selected participants reside in specialized facilities during Uyghur exchange programs, where daily smartphone use and exposure to various postures—such as standing, squatting, and natural movements—help refine AI accuracy. Adjustments account for about six degrees of head tilt, enhancing real-world performance.
Compensation and Logistics
Remuneration ranges from 20,000 to 70,000 yuan (roughly 430,000 to 1.5 million Korean won), depending on participation length, which averages 15 to 30 days but can extend to 60 days maximum. The Beijing-based trials run through August, with some recruits already enrolled ahead of the May 20 deadline.
Test subjects must maintain stable body conditions, avoiding factors like pregnancy that could alter facial structures. Those selected relocate to urban or rural heavy industry sites as needed.
Historical Context
This marks a continuation of prior efforts. In 2019, a similar “Ji Gu Byeol-2” program enlisted 36 male participants for up to 90 days of testing, demonstrating ongoing investment in AI-driven surveillance technologies.
