Journal article
On 3D face reconstruction via cascaded regression in shape space
Frontiers of information technology & electronic engineering, v 18(12), pp 1978-1990
Dec 2017
Abstract
Cascaded regression has been recently applied to reconstruct 3D faces from single 2D images directly in shape space, and has achieved state-of-the-art performance. We investigate thoroughly such cascaded regression based 3D face reconstruction approaches from four perspectives that are not well been studied: (1) the impact of the number of 2D landmarks; (2) the impact of the number of 3D vertices; (3) the way of using standalone automated landmark detection methods; (4) the convergence property. To answer these questions, a simplified cascaded regression based 3D face reconstruction method is devised. This can be integrated with standalone automated landmark detection methods and reconstruct 3D face shapes that have the same pose and expression as the input face images, rather than normalized pose and expression. An effective training method is also proposed by disturbing the automatically detected landmarks. Comprehensive evaluation experiments have been carried out to compare to other 3D face reconstruction methods. The results not only deepen the understanding of cascaded regression based 3D face reconstruction approaches, but also prove the effectiveness of the proposed method.
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Details
- Title
- On 3D face reconstruction via cascaded regression in shape space
- Creators
- Feng Liu - Sichuan UniversityDan Zeng - Sichuan UniversityJing Li - Sichuan UniversityFeng Liu - Drexel University, Computer ScienceQi-jun Zhao - Sichuan University
- Publication Details
- Frontiers of information technology & electronic engineering, v 18(12), pp 1978-1990
- Publisher
- Zhejiang University Press
- Number of pages
- 13
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000425158100004
- Scopus ID
- 2-s2.0-85042068080
- Other Identifier
- 991022048715004721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Information Systems
- Computer Science, Software Engineering
- Engineering, Electrical & Electronic