Adaptive Human-Centric Representation Learning for Robust and Fair Identity Systems under Distribution Shift
Human-centric identity systems deployed in real-world environments continuously encounter non-stationary conditions, such as changing illumination, background noise, device transitions, and physiological aging. These environmental and temporal variations introduce distribution shift, degrading system performance, eleva...