Abstract
Recently, it has been shown that excellent results can be achieved in both facial landmark localization and pose-invariant face recognition. These breakthroughs are attributed to the efforts of the community to manually annotate facial images in many different poses and to collect 3D facial data. In this paper, we propose a novel method for joint frontal view reconstruction and landmark localization using a small set of frontal images only. By observing that the frontal facial image is the one having the minimum rank of all different poses, an appropriate model which is able to jointly recover the frontalized version of the face as well as the facial landmarks is devised. To this end, a suitable optimization problem, involving the minimization of the nuclear norm and the matrix l1 norm is solved. The proposed method is assessed in frontal face reconstruction, face landmark localization, pose-invariant face recognition, and face verification in unconstrained conditions. The relevant experiments have been conducted on 8 databases. The experimental results demonstrate the effectiveness of the proposed method in comparison to the state-of-the-art methods for the target problems.
Original language | English |
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Title of host publication | Proceedings of The IEEE International Conference on Computer Vision (ICCV 2015) |
Place of Publication | USA |
Publisher | IEEE |
Pages | 3871-3879 |
Number of pages | 9 |
DOIs | |
Publication status | Published - Dec 2015 |
Event | IEEE International Conference on Computer Vision 2015 - Convention Center in Santiago, Santiago, Chile Duration: 7 Dec 2015 → 13 Dec 2015 http://pamitc.org/iccv15/ |
Publication series
Name | |
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Publisher | IEEE |
Workshop
Workshop | IEEE International Conference on Computer Vision 2015 |
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Abbreviated title | ICCV 2015 |
Country | Chile |
City | Santiago |
Period | 7/12/15 → 13/12/15 |
Internet address |
Keywords
- HMI-HF: Human Factors
- METIS-316049
- IR-99578
- EWI-26840