1. Novel framework: face feature selection algorithm for neonatal facial and related attributes recognition
- Author
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Saadullah Farooq Abbasi, Chen Chen, Xi Long, Chunmei Lu, Anum Nawaz, Saeed Akbarzadeh, Ronald M. Aarts, Linkai Tao, Bin Yin, Laishuan Wang, Muhammad Awais, Wei Chen, Signal Processing Systems, Systemic Change, Center for Care & Cure Technology Eindhoven, Biomedical Diagnostics Lab, and EAISI Foundational
- Subjects
Neonatal face detection ,neonatal pose estimation ,General Computer Science ,Computer science ,General Engineering ,Feature recognition ,facial feature selection (FFS) ,020207 software engineering ,Feature selection ,02 engineering and technology ,Facial recognition system ,video electroencephalogram (VEEG) ,Face (geometry) ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,General Materials Science ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,face neonatal attributes recognition (NFAR) ,Face detection ,lcsh:TK1-9971 ,Algorithm ,Pose - Abstract
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.
- Published
- 2020