以台灣年長者為對象之基於深度卷積類神經網路的自動臉部表情辨識

葉德緯

doi:10.6342/NTU202100072

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以台灣年長者為對象之基於深度卷積類神經網路的自動臉部表情辨識

Automatic Facial Expression Recognition for Taiwanese Elders with Deep Convolutional Neural Network

葉德緯 , Masters　　Advisor：鄭士康　　

英文 DOI： 10.6342/NTU202100072

臉部表情辨識；年長者看護；深度學習；遷移學習；可解釋人工智慧； Facial expression recognition ； Elderly care ； Deep learning ； Transfer learning ； Explainable artificial intelligence

Abstract │ Reference (85) │ Altmetrics

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This thesis has implemented a robust cross-dataset facial expression recognition system for Taiwanese elders based on a deep convolutional neural network (CNN). We investigate how aging affects the recognition of expressions for the CNN model. The CNN model is trained with combined datasets with data balancing to let the model be robust against unseen datasets. Also, the recognition performance of Taiwanese elders is improved via fine-tuning on small amounts of extra data. The CNN model outperforms human raters and the handcrafted-feature method on Taiwanese faces. The recognition accuracy difference between old faces and young faces is smaller than human raters and the handcrafted-feature method. Still, the CNN model perceives weaker emotional intensity on old faces, and this property is similar to human raters and the handcrafted-feature method. With the assistance of the XAI method, we can visualize the discriminative parts in the CNN model. The visualization implies that weakened facial muscles and wrinkles still affect the expression recognition for the CNN model. Overall, the CNN model resembles the handcrafted-feature method more than human raters when perceiving facial expressions for the elderly.

Reference ( 85 ) 〈TOP〉

[1] World Health Organization, “Global health and aging,” 2011. https://www.who.int/ageing/publications/global_health.pdf.
[2] C.-P. Liu, “Empathetic generative-based chatbot with emotion understanding via reinforcement learning,” Master’s thesis, Department of Electrical Engineering, National Taiwan University, 2020.
[3] J.-H. Jhan, “Empathetic and retrieval-based chatbot using deep reinforcement learning,” Master’s thesis, Graduate Institute of Communication Engineering, National Taiwan University, 2020.
[4] C. Sirithunge, A. B. P. Jayasekara, and D. Chandima, “Proactive robots with the perception of nonverbal human behavior: A review,” IEEE Access, vol. 7, pp. 77308–77327, 2019.
[5] S. Li and W. Deng, “Deep facial expression recognition: A survey,” IEEE Transactions on Affective Computing, 2020.

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