具學習能力之客製化臉部表情辨識系統研究

隨著電腦視覺技術的發展，基於視覺方式的偵測與辨識以提供人類與電腦 (或智慧型手機) 間非接觸式的互動技術，已成為日常生活應用的重要研究課題。　　本研究的主旨在開發一套「具學習能力之客製化臉部表情辨識系統」。傳統臉部表情辨識大多是根據一組含有許多使用者之大型資料庫進行訓練，本系統採取不同的方式，主要是設計來提供一種可客製化的方法，可以使用非負矩陣分解法學習並建立樣板臉部表情 (即無表情、高興、生氣、驚訝、傷心)。本系統可分為兩部分說明：(1) 臉部表情辨識方法；(2) 臉部表情辨識操作流程。為了進行系統評估，10位使用者參與本研究，研究結果顯示系統可初步成功的辨識五種特定表情。　　總結而言，本研究明顯證明本系統可提供臉部表情辨識的潛在解決方案，同時可以詮釋臉部表情因人而異的現象。

關鍵字

非負矩陣分解法；人機互動；電腦視覺；臉部表情辨識

並列摘要

Through the development of computer vision technology, vision-based detection & recognition capable of providing contactless interaction between human and computers (or smart phones) have become research of interest for daily applications. In this study, our objective is to develop a “customizable facial expression recognition system with learning capability”. Different from conventional facial expression recognition that is often trained using a large database with multiple users, our system is designed to provide a customizable approach that can learn and establish template facial expression (i.e., normal, happy, angry, surprise, and sad) using the Non-Negative Matrix Factorization for further recognition. The system can be described in two parts: (1) facial expression recognition method, and (2) facial expression recognition operation flow. For system evaluation, 10 users participated in this study and our results demonstrate preliminary success for recognizing the five specific facial expression. In summary, this study clearly shows that our system may provide a potential solution to facial expression recognition, addressing the inter-user variation in facial expression.

並列關鍵字

human-computer interaction ； non-negative matrix factorization. ； computer vision ； facial expression recognition

參考文獻

［1］ M. A. Turk and A. P. Pentland, “Face Recognition Using Eigenfaces,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 586-591, Jun 1991.

［3］ T. Zhang, B. Fang, Y. Y. Tang, G. He and J. Wen, “Topology Preserving Non-negative Matrix Factorization for Face Recognition,” IEEE Trans. Image Processing, vol. 17, pp. 574-584, Apr 2008.

［4］ S. G. Kong, J. Heo, B. R. Abidi, J. Paik, M. A. Abidi, “Recent Advances in Visual and Infrared Face Recognition—A Review,” Computer Vision and Image Understanding, vol. 97, no. 1, pp. 103-135, Jan. 2005.

［5］ J. Calder, A. M. Burton, P. Miller, A. W. Young and S. Akamatsu, “A Principal Component Analysis of Facial Expressions,” Vision Research, vol. 41, no. 9, pp. 1179-1208, Apr 2001.

［6］ I. Buciu and I. Pitas, “Application of Non-negative and Local Non Negative Matrix Factorization to Facial Expression Recognition,” 17th International Conference on Pattern Recognition, vol. 1, pp. 288-291, Aug 2004.

國際替代計量

具學習能力之客製化臉部表情辨識系統研究

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