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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Peng, Xi Zhang, Shaoting Yang, Yu Metaxas, Dimitris N. |
| Copyright Year | 2015 |
| Abstract | Face alignment, especially on real-time or large-scale sequential images, is a challenging task with broad applications. Both generic and joint alignment approaches have been proposed with varying degrees of success. However, many generic methods are heavily sensitive to initializations and usually rely on offline-trained static models, which limit their performance on sequential images with extensive variations. On the other hand, joint methods are restricted to offline applications, since they require all frames to conduct batch alignment. To address these limitations, we propose to exploit incremental learning for personalized ensemble alignment. We sample multiple initial shapes to achieve image congealing within one frame, which enables us to incrementally conduct ensemble alignment by group-sparse regularized rank minimization. At the same time, personalized modeling is obtained by subspace adaptation under the same incremental framework, while correction strategy is used to alleviate model drifting. Experimental results on multiple controlled and in-the-wild databases demonstrate the superior performance of our approach compared with state-of-the-arts in terms of fitting accuracy and efficiency. |
| Starting Page | 3880 |
| Ending Page | 3888 |
| File Size | 963742 |
| Page Count | 9 |
| File Format | |
| ISSN | 23807504 |
| e-ISBN | 9781467383912 |
| DOI | 10.1109/ICCV.2015.442 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-12-07 |
| Publisher Place | Chile |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Face Shape Robustness Adaptation models Optimization Real-time systems Lighting |
| Content Type | Text |
| Resource Type | Article |
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