Papers › ACR Loss: Adaptive Coordinate-based Regression Loss for Face Alignment
ACR Loss: Adaptive Coordinate-based Regression Loss for Face Alignment
Ali Pourramezan Fard, Mohammad H. Mahoor
Although deep neural networks have achieved reasonable accuracy in solving face alignment, it is still a challenging task, specifically when we deal with facial images, under occlusion, or extreme head poses. Heatmap-based Regression (HBR) and Coordinate-based Regression (CBR) are among the two mainly used methods for face alignment. CBR methods require less computer memory, though their performance is less than HBR methods. In this paper, we propose an Adaptive Coordinate-based Regression (ACR) loss to improve the accuracy of CBR for face alignment. Inspired by the Active Shape Model (ASM), we generate Smooth-Face objects, a set of facial landmark points with less variations compared to the ground truth landmark points. We then introduce a method to estimate the level of difficulty in predicting each landmark point for the network by comparing the distribution of the ground truth landmark points and the corresponding Smooth-Face objects. Our proposed ACR Loss can adaptively modify its curvature and the influence of the loss based on the difficulty level of predicting each landmark point in a face. Accordingly, the ACR Loss guides the network toward challenging points than easier points, which improves the accuracy of the face alignment task. Our extensive evaluation shows the capabilities of the proposed ACR Loss in predicting facial landmark points in various facial images.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Face Alignment | 300W | EF-3ACR | NME_inter-ocular (%, Challenge) | 5.36 | #32 of 48 | Archive leaderboard | report |
| Face Alignment | 300W | EF-3ACR | NME_inter-ocular (%, Common) | 3.36 | #32 of 48 | Archive leaderboard | report |
| Face Alignment | 300W | EF-3ACR | NME_inter-ocular (%, Full) | 3.75 | #32 of 48 | Archive leaderboard | report |
| Face Alignment | COFW | EF-3ACR | NME (inter-ocular) | 3.47% | #14 of 28 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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