Files and links (1)
SubmittedarXiv.org - Non-exclusive license to distribute, Open
Journal article
Joint Face Alignment and 3D Face Reconstruction with Application to Face Recognition
IEEE transactions on pattern analysis and machine intelligence, v 42(3), pp 664-678
01 Mar 2020
PMID: 30530314
Abstract
Face alignment and 3D face reconstruction are traditionally accomplished as separated tasks. By exploring the strong correlation between 2D landmarks and 3D shapes, in contrast, we propose a joint face alignment and 3D face reconstruction method to simultaneously solve these two problems for 2D face images of arbitrary poses and expressions. This method, based on a summation model of 3D faces and cascaded regression in 2D and 3D shape spaces, iteratively and alternately applies two cascaded regressors, one for updating 2D landmarks and the other for 3D shape. The 3D shape and the landmarks are correlated via a 3D-to-2D mapping matrix, which is updated in each iteration to refine the location and visibility of 2D landmarks. Unlike existing methods, the proposed method can fully automatically generate both pose-and-expression-normalized (PEN) and expressive 3D faces and localize both visible and invisible 2D landmarks. Based on the PEN 3D faces, we devise a method to enhance face recognition accuracy across poses and expressions. Both linear and nonlinear implementations of the proposed method are presented and evaluated in this paper. Extensive experiments show that the proposed method can achieve the state-of-the-art accuracy in both face alignment and 3D face reconstruction, and benefit face recognition owing to its reconstructed PEN 3D face.
Metrics
Details
- Title
- Joint Face Alignment and 3D Face Reconstruction with Application to Face Recognition
- Creators
- Feng Liu - Drexel University, Computer Science (Computing)Qijun Zhao - Sichuan UniversityXiaoming Liu - Michigan State UniversityDan Zeng - Sichuan University
- Publication Details
- IEEE transactions on pattern analysis and machine intelligence, v 42(3), pp 664-678
- Publisher
- IEEE
- Number of pages
- 15
- Grant note
- 2017YFB0802300 / National Basic Research Program of China (973 Program); National Key Research and Development Program of China (10.13039/501100012166) 61773270 / National Natural Science Foundation of China (10.13039/501100001809) 2013YQ49087904 / National Key Scientific Instrument and Equipment Development Projects of China (10.13039/501100012149)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Web of Science ID
- WOS:000525365300011
- Scopus ID
- 2-s2.0-85058181398
- Other Identifier
- 991021906501904721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Engineering, Electrical & Electronic