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|a eng
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| 100 |
1 |
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|a Yu, Xiang
|e verfasserin
|4 aut
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| 245 |
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|a Face Landmark Fitting via Optimized Part Mixtures and Cascaded Deformable Model
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|c 2016
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|a Date Completed 06.06.2017
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|a Date Revised 06.06.2017
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a This paper addresses the problem of facial landmark localization and tracking from a single camera. We present a two-stage cascaded deformable shape model to effectively and efficiently localize facial landmarks with large head pose variations. In initialization stage, we propose a group sparse optimized mixture model to automatically select the most salient facial landmarks. By introducing 3D face shape model, we apply procrustes analysis to provide pose-aware landmark initialization. In landmark localization stage, the first step uses mean-shift local search with constrained local model to rapidly approach the global optimum. The second step uses component-wise active contours to discriminatively refine the subtle shape variation. Our framework simultaneously handles face detection, pose-robust landmark localization and tracking in real time. Extensive experiments are conducted on both laboratory environmental databases and face-in-the-wild databases. The results reveal that our approach consistently outperforms state-of-the-art methods for face alignment and tracking
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|a Journal Article
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|a Huang, Junzhou
|e verfasserin
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|a Zhang, Shaoting
|e verfasserin
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| 700 |
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|a Metaxas, Dimitris N
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|t IEEE transactions on pattern analysis and machine intelligence
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|g 38(2016), 11 vom: 05. Nov., Seite 2212-2226
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