Real-time patient facial expression recognition using convolutional neural network

Xin Chen; Yutong Qian; Shilei Fu; Qian Song

doi:10.1117/12.2547836

27 November 2019 Real-time patient facial expression recognition using convolutional neural network

Xin Chen, Yutong Qian, Shilei Fu, Qian Song

Proceedings Volume 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence; 113210R (2019) https://doi.org/10.1117/12.2547836
Event: The Second International Conference on Image, Video Processing and Artifical Intelligence, 2019, Shanghai, China

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.

Citation Download Citation

Xin Chen, Yutong Qian, Shilei Fu, and Qian Song "Real-time patient facial expression recognition using convolutional neural network", Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113210R (27 November 2019); https://doi.org/10.1117/12.2547836

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