Sensors, Vol. 19, Pages 4124: Boosting Depth-Based Face Recognition from a Quality Perspective

Sensors, Vol. 19, Pages 4124: Boosting Depth-Based Face Recognition from a Quality Perspective Sensors doi: 10.3390/s19194124 Authors: Zhenguo Hu Penghui Gui Ziqing Feng Qijun Zhao Keren Fu Feng Liu Zhengxi Liu 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.
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research