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Real-time patient facial expression recognition using convolutional neural network
Author(s): Xin Chen; Yutong Qian; Shilei Fu; Qian Song
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Paper Abstract

Real-time monitoring of patients in hospital is of great importance, as it serves as an alarm of emergence condition. However, all-day company of carers or monitor is costly, and a waste of resources. With the development of deep learning, it is worthy of consideration to use low-cost real-time target recognition method in machine learning instead. This paper proposes to monitor the state of the patients via facial expression recognition. In order to that, a two-stage approach, i.e. detection of the face of the patient and classification the facial expression, is proposed. The face detector relies on the Harr feature, and is pre-trained. Then the detected face are classified either as “normal” or “abnormal” via a convolutional neural network. The training and test data are collected in real scene by mobile phone. The experimental results show an accuracy of 83% is achieved in test set.

Paper Details

Date Published: 27 November 2019
PDF: 5 pages
Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113210R (27 November 2019); doi: 10.1117/12.2547836
Show Author Affiliations
Xin Chen, Princeton High School (United States)
Yutong Qian, Fudan Univ. (China)
Shilei Fu, Fudan Univ. (China)
Qian Song, Fudan Univ. (China)


Published in SPIE Proceedings Vol. 11321:
2019 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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