Conference proceeding
On Mugshot-based Arbitrary View Face Recognition
2018 24th International Conference on Pattern Recognition (ICPR), v 2018-, pp 3126-3131
Aug 2018
Abstract
Despite the wide usage of mugshot images in forensic applications, they are underutilized in existing automated face recognition systems. In this paper, we propose a novel mugshot-based arbitrary view face recognition method. Our approach reconstructs full 3D faces via cascaded regression in shape space with efficient seamless texture recovery. Unlike existing methods, it makes full use of the frontal and profile views available in mugshot images, and thus generates accurate and realistic 3D faces. Multi-view face images are synthesized from the reconstructed 3D faces to enlarge the gallery so that arbitrary view faces can be better recognized. Evaluation experiments were conducted on BFM and Multi-PIE databases by using state-of-the-art deep learning (DL) based face matchers. The results demonstrate the effectiveness of our proposed method and show that DL-based face matchers can benefit from mugshot images and the reconstructed 3D faces, especially for recognizing large off-angle faces.
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Details
- Title
- On Mugshot-based Arbitrary View Face Recognition
- Creators
- Jie Liang - Sichuan UniversityFeng Liu - Sichuan UniversityHuan Tu - Sichuan UniversityQijun Zhao - Sichuan UniversityFeng Liu - Drexel University, Computer Science (Computing)Anil K. Jain - Michigan State University
- Publication Details
- 2018 24th International Conference on Pattern Recognition (ICPR), v 2018-, pp 3126-3131
- Publisher
- IEEE
- Number of pages
- 6
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Web of Science ID
- WOS:000455146803022
- Scopus ID
- 2-s2.0-85059781015
- Other Identifier
- 991022048714704721
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