In this study we propose a framework to enhance Heart Rate (HR) estimation accuracy from facial video. It integrates a two-stage geometric stabilization pipeline with dense facial tessellation to mitigate motion. Validated on the challenging COHFACE dataset, the approach achieves a Mean Absolute Error (MAE) of 1.50 bpm, a Root Mean Square Error (RMSE) of 3.07 bpm, and a Pearson Correlation Coefficient (PCC) of 0.97 on the test set. The method demonstrated robustness across diverse lighting conditions, outperforming traditional algorithms and achieving parity with state-of-the-art deep learning models, while offering an interpretable solution for contactless health monitoring. See more.