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Boosting Depth-Based Face Recognition from a Quality Perspective.

ABSTRACT: Face recognition using depth data has attracted increasing attention from both academia and industry in the past five years. Previous works show a huge performance gap between high-quality and low-quality depth data. Due to the lack of databases and reasonable evaluations on data quality, very few researchers have focused on boosting depth-based face recognition by enhancing data quality or feature representation. In the paper, we carefully collect a new database including high-quality 3D shapes, low-quality depth images and the corresponding color images of the faces of 902 subjects, which have long been missing in the area. With the database, we make a standard evaluation protocol and propose three strategies to train low-quality depth-based face recognition models with the help of high-quality depth data. Our training strategies could serve as baselines for future research, and their feasibility of boosting low-quality depth-based face recognition is validated by extensive experiments.

SUBMITTER: Hu Z

PROVIDER: S-EPMC6806307 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Boosting Depth-Based Face Recognition from a Quality Perspective.

Hu Zhenguo Z Gui Penghui P Feng Ziqing Z Zhao Qijun Q Fu Keren K Liu Feng F Liu Zhengxi Z

Sensors (Basel, Switzerland) 20190923 19

Face recognition using depth data has attracted increasing attention from both academia and industry in the past five years. Previous works show a huge performance gap between high-quality and low-quality depth data. Due to the lack of databases and reasonable evaluations on data quality, very few researchers have focused on boosting depth-based face recognition by enhancing data quality or feature representation. In the paper, we carefully collect a new database including high-quality 3D shapes ...[more]

PMID: 31548515

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