Department of Electronic Engineering, Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai, China
Department of Electronic Engineering, Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai, China
Xi Long
Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, AZ, The Netherlands
Bin Yin
Connected Care and Personal Health Department, Philips Research, Shanghai, China
Anum Nawaz
Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China
Saadullah Farooq Abbasi
Department of Electronic Engineering, Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai, China
Saeed Akbarzadeh
Department of Electronic Engineering, Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai, China
Linkai Tao
Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, AZ, The Netherlands
Chunmei Lu
Department of Neonatology, Children’s Hospital of Fudan University, Shanghai, China
Department of Electronic Engineering, Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai, China
In recent times, with the advancement of digital imaging, automatic facial recognition has been intensively studied for adults, while less for neonates. Due to the miniature facial structure and facial attributes, newborn facial recognition remains a challenging area. In this paper, an automatic video-based Neonatal Face Attributes Recognition (NFAR) approach in a hierarchical framework is proposed by coalescing the intensity-based method, pose estimation, and novel dedicated neonatal Face Feature Selection (FFS) algorithm. The intensity-based method is used for face detection, followed by the facial pose estimation algorithm and FFS are dedicated to neonatal pose and face feature recognition, respectively. In this study, video-data of 19 neonates' were collected from the Children's Hospital affiliated to Fudan University, Shanghai, to evaluate the proposed NFAR approach. The results show promising performance to detect the neonatal face, pose estimation (-45°, 45°), and facial features (nose, mouth, and eyes) recognition. The NFAR approach exhibits a sensitivity, accuracy, and specificity of 98.7%, 98.5%, and, 95.7% respectively, for the newborn babies at the frontal (0°) facial region. The neonatal face and its attributes recognition can be expected to detect neonate's medical abnormalities unobtrusively by examining the variation in newborn facial texture pattern.