A Novel Facial Thermal Feature Extraction Method for Non-Contact Healthcare System
| Autor: | Tz-Heng Hsu, Chao-Chun Chen, Gwo-Jiun Horng, Zhi-Hao Wang, Gwo-Jia Jong |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
021110 strategic
defence & security studies General Computer Science business.industry Computer science Deep learning Feature extraction 0211 other engineering and technologies General Engineering deep learning Pattern recognition 02 engineering and technology Image (mathematics) thermal feature extraction Non-contact healthcare system Feature (computer vision) Face (geometry) 0202 electrical engineering electronic engineering information engineering RGB color model 020201 artificial intelligence & image processing General Materials Science Artificial intelligence lcsh:Electrical engineering. Electronics. Nuclear engineering business lcsh:TK1-9971 GoogLeNet |
| Zdroj: | IEEE Access, Vol 8, Pp 86545-86553 (2020) |
| ISSN: | 2169-3536 |
| Popis: | The non-contact healthcare system is a system that can avoid germ infection, and can also provide comfortable and convenient health care services for the caregivers. In the current thermal imaging research, local or small area images are used to represent the overall temperature information of the participants, but the temperature feature of the face should not only be used in a small part and ignore other parts. The facial thermal image can show clear temperature feature but is not conducive to meaningful feature extraction. Therefore, this research proposed a novel facial thermal image feature extraction method, which is used facial landmarks to detect and cut 12 blocks to establish a new feature matrix based on color mean values and standard deviation values. It can establish clear features on facial thermal images. The core part of the proposed healthcare system is the use of a deep learning framework, which is based on CAFFE under the DIGITS platform. The CAFFE runs the classic CNN, GoogLeNet. Based on the acquired images and new feature types, four models were trained, which were used for the raw RGB image, raw thermal image, RGB feature image, and thermal feature image. In the experiment, 800 images were used for training and validation, and 200 images were used for testing. An additional 40 images were used for random testing. The experimental results show that RGB images cannot be effectively used, thermal images can effectively predict the health status, and thermal feature images have the highest prediction accuracy. |
| Databáze: | OpenAIRE |
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