Papers › Deep Structured Prediction for Facial Landmark Detection
Deep Structured Prediction for Facial Landmark Detection
Lisha Chen, Hui Su, Qiang Ji
Existing deep learning based facial landmark detection methods have achieved excellent performance. These methods, however, do not explicitly embed the structural dependencies among landmark points. They hence cannot preserve the geometric relationships between landmark points or generalize well to challenging conditions or unseen data. This paper proposes a method for deep structured facial landmark detection based on combining a deep Convolutional Network with a Conditional Random Field. We demonstrate its superior performance to existing state-of-the-art techniques in facial landmark detection, especially a better generalization ability on challenging datasets that include large pose and occlusion.
In Syntology View this paper on Syntology: its repositories, every harvested function with whether it ran, its licence and the call to fetch it.
Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Face Alignment | 300W | CNN-CRF | NME_inter-ocular (%, Challenge) | 4.84 | #23 of 48 | Archive leaderboard | report |
| Face Alignment | 300W | CNN-CRF | NME_inter-ocular (%, Common) | 2.93 | #23 of 48 | Archive leaderboard | report |
| Face Alignment | 300W | CNN-CRF | NME_inter-ocular (%, Full) | 3.30 | #23 of 48 | Archive leaderboard | report |
| Face Alignment | 300W | CNN-CRF | NME_inter-pupil (%, Challenge) | 6.98 | #23 of 48 | Archive leaderboard | report |
| Face Alignment | 300W | CNN-CRF | NME_inter-pupil (%, Common) | 4.06 | #23 of 48 | Archive leaderboard | report |
| Face Alignment | 300W | CNN-CRF | NME_inter-pupil (%, Full) | 4.63 | #23 of 48 | Archive leaderboard | report |
| Facial Landmark Detection | 300W | CNN-CRF (Inter-ocular Norm) | NME | 3.30 | #8 of 15 | 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.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections