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Optimizing Facial Landmark Detection by Facial Attribute Learning
| Content Provider | Semantic Scholar |
|---|---|
| Author | Zhang, Zhanpeng Luo, Ping Loy, Chen Change Tang, Xiaoou |
| Copyright Year | 2014 |
| Abstract | Instead of treating the facial landmark detection task as a single and independent problem, we investigate the possibility of improving detection robustness through multi-task learning. Specifically, we wish to optimize facial landmark detection together with multiple facial attributes learning. This is non-trivial since different tasks have different learning difficulties and convergence rates. To address this problem, we formulate a novel tasks-constrained deep model, with task-wise early stopping to facilitate learning convergence. Extensive evaluations show that the proposed task-constrained learning (i) outperforms existing methods, especially in dealing with faces with severe occlusion and pose variation, and (ii) reduces model complexity drastically compared to the state-of-the-art method based on cascaded deep model [5]. 1 |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://www.cc.gatech.edu/~parikh/PnA2014/posters/Posters/ZhanpengZhangPnA2014.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
