A Large-Scale Clinical Benchmark of ResNet-based Deep Models for Newborn Face Recognition.
Annu Int Conf IEEE Eng Med Biol Soc
; 2023: 1-4, 2023 07.
Article
en En
| MEDLINE
| ID: mdl-38082835
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.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Identificación Biométrica
/
Reconocimiento Facial
Límite:
Humans
/
Infant
/
Newborn
Idioma:
En
Revista:
Annu Int Conf IEEE Eng Med Biol Soc
Año:
2023
Tipo del documento:
Article
Pais de publicación:
Estados Unidos