跳到主要內容

臺灣博碩士論文加值系統

(34.204.169.230) 您好!臺灣時間:2024/02/28 08:20
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:潘奕安
研究生(外文):Yi-An Pan
論文名稱:低解析度影像序列之自動化表情辨識系統
論文名稱(外文):Automatic Facial Expression Recognition System in Low Resolution Image Sequence
指導教授:郭淑美郭淑美引用關係
指導教授(外文):Shu-Mei Guo
學位類別:碩士
校院名稱:國立成功大學
系所名稱:資訊工程學系碩博士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:中文
論文頁數:60
中文關鍵詞:關鍵影像選取光流表情辨識
外文關鍵詞:key-frame selectionoptical flowfacial expression recognition
相關次數:
  • 被引用被引用:15
  • 點閱點閱:321
  • 評分評分:
  • 下載下載:65
  • 收藏至我的研究室書目清單書目收藏:1
  『Computer Will Be More Human』被PC Magazine眾資訊專家預測為未來資訊界的首要技術發展趨勢,更人性化的人機介面成了現今科學家們努力追求的目標,而全自動化表情辨識乃為達成此目標之關鍵技術。
  本論文提供了一個完整的表情辨識系統,包含了四個部分:基於色彩及相對位置的人臉偵測、包含特徵點與區域特性的面部特徵擷取、基於光流的關鍵影像選取,以及模糊類神經網路的表情辨識。
在人臉偵測的部分,充分結合色彩與幾何關係,改進了先前搜尋近似人臉比例之膚色橢圓之方法,直接以眼睛嘴巴所形成之三角關係進行臉部特徵搜尋並進一步確認人臉區域。在特徵擷取的部分,一般來說,本論文採用混合多種特徵的方式來提升辨識效果,包含了靜態的特徵間之距離以及特定區域的不變矩,由實驗結果顯示了良好的辨識效果。另外,在辨識的過程,嘗試加入了模糊化的概念,以模糊類神經網路來進行分類。
  實驗結果顯示,相較於其他的方法,由於多了關鍵影像選取的程序,系統不必針對每一個畫面做辨識,僅在表情產生變化時啟動,大幅縮減了辨識的時間,另外,由於關鍵影像只出現在表情強度最大的時候,亦減少了許多因特徵不明確而誤判的現象,大大提高辨識率。
  According to predictions of computer science professionals of PC Magazine, “Computer Will Be More Human” is going to be the first of all developments within the computer science industry in the near future. Particularly, the automatic facial expression recognition system is the key technology to approach this goal. A completely automatic facial expression recognition system is proposed in this thesis, which consists of four partitions: a color and geometry-based face detection process, a facial feature extraction process including points and regional features, an optical flow based key-frame selection process, and an expression recognition process by the fuzzy neural network. For the face detection, instead of the conventional method for detecting the elliptic skin color region, a novel approach by searching facial features and examining a triangular geometric relationship is proposed to confirm the exact facial area. Concerning the facial feature extraction, the multi-feature mechanism, which includes the optical flow (describe the motion situation), feature points (describe the distribution of features), and invariant moments that represent the regional information, is presented to have a high identification efficiency. Besides, via combining the concept of the fuzzy mechanism and the neural network approach, the fuzzy neural network approach is proposed for the classification process. Experiment results show that due to the proposed “Key-frame selection” mechanism, the recognition system only operates while the expression is changed, not frame-by-frame, which significantly decreases enormous time for the recognition process. Moreover, since the “Key-frame” only appears for the maximal intensity of facial expression, it enables to raise the recognition rate as expected and mitigate the misclassified situation caused by indefinite features.
目錄 IV
圖目錄 VI
表目錄 1
第一章 緒論 2
1.1 研究動機 2
1.2 表情辨識系統概述 3
1.3 研究貢獻 4
1.4 論文架構 6
第二章 相關研究與背景 7
2.1人臉偵測 7
2.2人臉特徵擷取 9
2.3臉部特徵定位 9
2.4表情分類與辨識 10
第三章 人臉偵測 12
3.1光線補償(Lighting Compensation) 13
3.2 膚色分割 14
3.3 人臉可能區域之選擇 17
3.4 嘴巴偵測 18
3.4.1顏色定位 18
3.4.2分割嘴巴區域 19
3.5 眼睛可能區域偵測 20
3.5.1前處理 20
第四章 臉部特徵擷取 24
4.1 五官特徵點擷取 24
4.1.1眼睛特徵點偵測 25
4.1.2眉毛特徵點偵測 26
4.1.3 嘴巴特徵點偵測 26
4.2 特徵距離(Feature Distances) 27
4.3 光流(Optical Flow) 29
4.3.1 光流方程式 30
4.4 不變矩(Invariant Moment) 33
第五章 表情分類與辨識 36
5.1 關鍵影像的選取 36
5.2 倒傳遞類神經網路 42
5.2.1 簡介 42
5.2.2 網路架構 42
5.3 模糊類神經網路(Fuzzy-Neural Network) 43
第六章 實驗分析與結果 47
6.1資料庫 47
6.2實驗規劃 47
6.3實驗結果 48
6.3.1不同特徵值的實驗 48
6.3.2模糊類神經網路與倒傳遞類神經網路之比較 54
6.3.3關鍵影像選取 54
第七章 結論與展望 56
7.1結論 56
7.2後續研究之建議 57
參考文獻 58
[1]Z. Zhang, M. Lyons, M. Schuster, and S. Akamatsu. “Comparison between geometry-based and Gabor-wavelets-based facial expression recognition using multi-layer perceptron.” Proc. Third IEEE International Conference on Automatic Face and Gesture Recognition (FG'98), pp. 454-459, Nara, Japan, April 14-16, 1998
[2]Shinjiro Kawato, Jun Ohya, ”Automatic Skin-color Distribution Extraction for Face Detection and Tracking”, Proc. Int. Conf. On Signal Processing, Vol.II, pp.1415-1418, 2000
[3]J.-C. Terrillon, M. N. Shirazi, H. Fukamachi, and S. Akamatsu, ”Somparative Performance of Different Skin Chrominance Models and Chrominance Spaces for Automatic Detection of Human Faces in Color Images”, In Proc of the International Conference on Face and Gesture Recognition, pp. 54-61, Grenoble, France, 2000
[4]M. Soriano, S. Huovinen, B. Martinkauppi, M.Laaksonen,”Using The Skin Locus To Cope With Changing Illumination Conditions in Color-Based Face Tracking”, Proc. IEEE Nordic Signal Processing Symposium(NORSIG 2000), pp.383-386, Kolmarden, Sweden,2000
[5]lbiol, L. Torres, E. Delp, C. Bouman, “A Simple and Efficient Face Detection Algorithm for Video Database Applications”, IEEE International Conference on Image Processing, Vancouver, Canada, September 10-13, 2000
[6]Haiyuan Wu, Qian Chen, and Masahiko Yachida, “Face Detection From Color Images Using Fuzzy Pattern Matching Method”, IEEE Trans. On Pattern Analysis and Machine Intelligence, Vol.21, No.6, pp.557-563, June,1999
[7]H. Ishii, M. Fukumi, N. Akamatsu, “Face Detection Based on Skin Color Information in Visual Scenes by Neural Networks”, IEEE SMC ’99 Conference Proceedings. 1999 IEEE International Conference on, Vol.5, pp.557-563, 1999
[8]D. Anifantis, “A Neural Network Method For Accurate Face Detection on Arbitrary Images”, Wire Communications Laboratory, Univ. of Patras, 1999
[9]Yao-Hong Tasi, Yea-Shuan Huang “Fast Face Detection From Color Images” CVGIP,2001
[10]Chin-Chung Han, Hong-Yuan Mark Liao, Kuo-Chung Yu, and Liang-Hua Chen, ”Fast Face Detection Via Morphology-based Pre-processing”, Academia Sinica, Taipei, Taiwan
[11]Chiun Lin, Kuo-Chin Fan, “Human Face Detection Using Triangle Relationship”, 15th International Conference on Pattern Recognition, Vol.2, pp.945-948, September 2000
[12]M.Hasegawa, Y.Nasu, “A Method of Face Images by Using Multiplex Resolution Image”, IEICE Tech, Report PRU 89-26, pp.57-60, Japanse
[13]Rein-Lien Hsu, Abdel-Mottaleb. M., Jain,A.K., “Face Detection in Color Images” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.24, Issue:5, pp.696-706, May 2002
[14]Yao, Hongxun and Wen Gao, “Face Detection and Location Based on Skin Chrominance and Lip Chrominance Transformation From Color Images”, Pattern Recognition, Vol.34, number 8, pp.1555-1564, 2001
[15]Jeonghee Park, Jungwon Seo, Dongun An, Seongjong Chung, “Detection of Human Faces Using Skin Color and Eyes”, IEEE International Conference on Multimedia and Expo. Vol.1, pp.133-136, 2000
[16]S.H.Jeng, H.M. Liao, Y.T. Liu, and M.Y. Chern, “An Efficient Approach for Facial Feature Detection Using Geometrical Face Model”, Int. Proceedings of the ICPR 1996, pp.1739-1755, 1993
[17]H. Hongo, A. Murata, K. Yamamoto, “Consumer Products User Interface Using Face and Eye Orientation”, IEEE International Symposium on Consumer Electronics, pp.87-90, 1997
[18]Y.Mitsukura,; E.Fukumi,; N.Akamatsu, “A Design of Face Detection System by Using Lip Detection Neural Network and Skin Distinction Neural Network”, IEEE International Conference on Systems, Man, and Cybernetics, Vol.4, pp.2789-2793, 2000
[19]C. H. Morimoto; M. Flickner, “Real-time Multiple Face Detection Using Active Illumination”, Fourth IEEE International Conference on Automatic Face and Gesture Recognition, pp.8-18, 2000
[20]王進德,蕭大全,類神經網路與模糊控制理論入門,台北,全華科技股份有限公司,2001
[21]Arun D. Kulkarni, “Computer Vision and Fuzzy-Neural Systems”, Prentice Hall, 2001
[22]Rafael C. Gonzalez, Richard E. Woods, “Digital Image Processing”, Prentice Hall, 2002
[23]Horn BK P, Schunk GB, “Determining Optical Flow”, Artificial Intelligence, vol1.17, pp.185-203, 1981
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top