According to an energy-efficient pupil tracking method and device based on the simplification of a cascade regression forest proposed in the present invention, a lightweight cascade coarse-to-fine regression forest (CCD-RF) is generated by applying a rule distillation according to feature importance-based rule contribution to an initial CCD-RF which is composed of a cascade multilayer using a plurality of regression forests and has been trained on pupil position using a coarse-to-fine method, and the lightweight CCD-RF is used to predict pupil position from an image. Thus, the number of parameters and the amount of calculations can be reduced and energy consumption can be minimized while maintaining strong performance through the removal of unnecessary rules.
ENERGY-EFFICIENT PUPIL TRACKING METHOD AND DEVICE BASED ON SIMPLIFICATION OF CASCADE REGRESSION FOREST