Boosting Depth-Based Face Recognition from a Quality Perspective

• Published in: Sensors (Basel, Switzerland) Vol. 19; no. 19; p. 4124
• Main Authors: Hu, Zhenguo, Gui, Penghui, Feng, Ziqing, Zhao, Qijun, Fu, Keren, Liu, Feng, Liu, Zhengxi
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 23.09.2019; MDPI
• Subjects: Accuracy; Algorithms; Color imagery; data quality; Databases, Factual; deep models; depth-based face recognition; Face recognition; Facial Recognition - physiology; Facial recognition technology; Humans; Image quality; Pattern Recognition, Automated - methods; Sensors; data quality; depth-based face recognition; database; deep models
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19194124

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.