Towards Highly Accurate and Stable Face Alignment for High-Resolution Videos

Understand

In recent years, heatmap regression based models have shown their effectiveness in face alignment and pose estimation.

• However, Conventional Heatmap Regression (CHR) is not accurate nor stable when dealing with high-resolution facial videos, since it finds the maximum activated location in heatmaps which are generated from rounding coordinates, and thus leads to quantization errors when scaling back to the original high-resolution space.
• In this paper, we propose a Fractional Heatmap Regression (FHR) for high-resolution video-based face alignment.
• The proposed FHR can accurately estimate the fractional part according to the 2D Gaussian function by sampling three points in heatmaps.