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  • Theses

基於動態臉部特徵在視訊中之臉部表情辨識研究

Research on Facial Expression Recognition using Dynamic Facial Features in Video Sequences

Advisor : 張元翔

Abstracts


自動臉部表情辨識在電腦視覺領域中仍然是具有挑戰性的問題。傳統的表情辨識通常使用靜態的單張影像,因此常受到不同照明、不同的視角及表情的細微差異所影響。為了克服這種不適定性問題,本研究旨在發展一套新方法,使用視訊中之動態臉部特徵來辨識五種特定的臉部表情(即無表情、生氣、高興、傷心及驚訝)。研究方法包括:(1) 臉部偵測;(2) 臉部特徵偵測;(3) 臉部特徵擷取;及(4) 分類模型。此外,整合有限狀態機針對臉部特徵之動態變化進行處理。初步研究結果顯示,本方法可以達到81.4%之整體辨識率。歸納而言,本研究提出一項自動臉部表情辨識的潛在解決方案。

Parallel abstracts


Automatic facial expression recognition remains a challenging issue in the field of computer vision. Traditionally, recognition is typically based on the use of static single-image, thus is often subject to varying illumination, different view, and subtle difference of facial expressions. To overcome such an ill-posed problem, this study is aimed to develop a novel method for the recognition of five specific facial expressions (i.e., neutral, angry, happy, sad, and surprise) using dynamic facial features in video sequences. Our method includes: (1) face detection; (2) facial feature detection; (3) facial feature extraction; and (4) classification model. In addition, a finite-state-machine is integrated for processing dynamic changes of facial features. Our preliminary results indicated that our method could achieve an overall recognition of 81.4%. In summary, we have presented a potential solution to automatic facial expression recognition.

References


[1] D. Marius, S. Pennathur and K. Rose, “Face Detection Using Color Thresholding, and Eigenimage Template Matching,” EE368: Digital Image Processing Project, Stanford University, Standford, CA, 2003.
[7] W. Zhao, R. Chellappa, P. J. Phillips, A. Rosenfeld, “Face Recognition: A Literature Survey,” ACM Computing Surveys, vol.35, issue 4, pp.399-459, 2003.
[2] P. Viola and M. J. Jones, “Robust Real-Time Face Detection,” International Journal of Computer Vision, vol. 57, no.2, pp.137–154, 2004.
[3] 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, issue 1, pp.103-135, 2005.
[4] J. Wu, S. C. Brubaker, M. D. Mullin, J. M. Rehg, “Fast Asymmetric Learning for Cascade Face Detection,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.30, no.3, pp.369-382, 2008.

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