A Large-Scale Clinical Benchmark of ResNet-based Deep Models for Newborn Face Recognition

Changyi Wu1; Dongmin Huang1; Lirong Ren2; Hongzhou Lu3; Wenjin Wang1

10.1109/EMBC40787.2023.10340883

2023

45th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

2375-7477

1558-4615

979-8-3503-2448-8

2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

1-4

24-27 July 2023

Sydney, Australia

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Newborn face recognition is a meaningful application for obstetrics in the hospital, as it enhances security measures against infant swapping and abduction through authentication protocols. Due to limited newborn face datasets, this topic was not thoroughly studied. We conducted a clinical trial to create a dataset that collects face images from 200 newborns within an hour after birth, namely NEWBORN200. To our best knowledge, this is the largest newborn face dataset collected in the hospital for this application. The dataset was used to evaluate the four latest ResNet-based deep models for newborn face recognition, including ArcFace, CurricularFace, MagFace, and AdaFace. The experimental results show that AdaFace has the best performance, obtaining 55.24% verification accuracy at 0.1% false accept rate in the open set while achieving 78.76% rank-1 identification accuracy in a closed set. It demonstrates the feasibility of using deep learning for newborn face recognition, also indicating the direction of improvement could be the robustness to varying postures.

Deep learning Pediatrics Protocols Hospitals Face recognition Benchmark testing Clinical trials

第一

英语

EI ; CPCI-S

National Key R&D Program of China[2022YFC2407800]

Computer Science ; Engineering

Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical

WOS:001133788303229

20240215361410

Deep learning ; Hospitals

Ergonomics and Human Factors Engineering:461.4 ; Hospitals, Equipment and Supplies:462.2

IEEE

1.Department of Biomedical Engineering, Southern University of Science and Technology, China
2.Department of Obstetrics, Baoan Hospital of Traditional Chinese Medicine in Shenzhen, China
3.The Third People’s Hospital of Shenzhen, China

Changyi Wu,Dongmin Huang,Lirong Ren,et al. A Large-Scale Clinical Benchmark of ResNet-based Deep Models for Newborn Face Recognition[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2023:1-4.