Constrained Rotation Optimization: Revisiting Crop-Based Gaze Estimation

Appearance-based gaze estimation typically relies on facenormalization to reduce appearance variability, but this requires costlyand error-prone landmark detection and head pose estimation. Whilecrop-based alternatives have been explored, their geometric propertiesand performance trade-offs relative to normalization remain underex-plored. In this work, we provide the first systematic comparison of nor-malization versus crop-based gaze estimation. To enable fair compari-son, we formalize the crop-based approach through Constrained Rota-tion Optimization (CROp), making its geometric transformation explicitand comparable to normalization. We further adopt multi-task learningto recover head pose information lost in cropping. Through extensiveexperiments across various datasets, head pose distributions, and pre-processing conditions, we identify the conditions under which each ap-proach excels. CROp shows advantages under extreme poses and noisydetection, while normalization benefits from landmark-based refinementin moderate conditions. Our analysis provides practical guidelines forchoosing preprocessing strategies in real-world gaze estimation systems.