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研究生:陳嘉雄
研究生(外文):Chia-Hsiung Chen
論文名稱:多角度人臉之二階段辨識
論文名稱(外文):Two-Stage Recognition of Multi-orientation Faces
指導教授:江政欽江政欽引用關係
指導教授(外文):Cheng-Chin Chiang
學位類別:碩士
校院名稱:國立東華大學
系所名稱:資訊工程學系
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:2002
畢業學年度:90
語文別:中文
論文頁數:66
中文關鍵詞:圖形識別人臉辨識類神經網路二階段辨識
外文關鍵詞:Two-Stage RecognitionPattern RecognitionPCAFLDWaveletNeural Network
相關次數:
  • 被引用被引用:15
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  • 下載下載:193
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本論文的目的是考慮多角度變化的人臉影像辨識。在人臉的辨識研究上,雖然有許多特徵抽取方式與分類方法的不同,但是對於人臉訓練與辨識的方式卻大同小異,都屬於一階段辨識法,這對於多角度的變化容易造成彼此樣本間的干擾。而本論文主要針對多角度人臉之辨識提出二階段之改良辨識法。
二階段辨識法主要可分為大分類與細分類兩階段。大分類是粗糙的歸納,用途是希望藉由大分類把相似特性特性分群,理想的情況是能依照角度分開,把相似角度的影像分為同一群。細分類是精確的比對,用途是將影像經由大分類後判斷所屬的群集,然後再對群集做細分類的分析轉換做最後的分類結果。這樣做的好處是可以避免多角度的干擾,經過大分類後所得到群集裡的樣本都是相似角度的特徵。
由實驗的結果可以發現,二階段辨識法在多角度的人臉影像辨識確實可以提升辨識率。因此未來可以藉由此方式擴展到多表情、多光線之多階段辨識法。
The goal of this paper is to improve the recognition of multi-orientation faces. In previous approaches for face recognition, although there exist many different methods for feature extractions and classifications, most of them belong to one-stage recognition. This kind of approach usually causes interference between samples with large variations. Hence, this paper proposes a two-stage method for multiple orientations.
The main concept of two-stage recognition is to recognize faces through coarse and fine classifications. The coarse classification, which is usually termed as clustering, is separate face patterns into different clusters according to their orientations. The fine classification is to accurately identify the face patterns as the target individuals. The benefit of this approach is the avoidance of interferences between multi-orientation faces.
Our experimental results on two databases show that the two-stage recognition indeed improves the recognition rate over the one-stage methods. In the future, we will expand the method to multi-stage recognition for face images with multiple expressions and under varying light conditions based on the same concepts.
摘要 .....................................1
ABSTRACT .................................2
誌謝(Acknowledgement) ..................3
目錄 .....................................4
圖目錄 ...................................6
表目錄 ...................................7
簡介 .....................................8
1.1 概論 .................................8
1.2 相關技術 .............................9
1.3 人臉辨識工作流程 ....................10
1.4 章節組織 ............................12
第2章 前置處理與特徵抽取 ................13
2.1 前置處理 ............................13
2.1.1 影像大小正規化 ....................13
2.1.2 灰階正規化 ........................14
2.2 特徵抽取 ............................15
2.2.1 原始影像特徵 ......................15
2.2.2 Eigenface特徵 .....................16
2.2.3 Fisherface特徵 ....................23
2.2.4 Wavelet特徵 .......................32
第3章 分類方法 ..........................35
3.1 距離函數 ............................35
3.1.1 Manhattan .........................35
3.1.2 Euclidean .........................36
3.1.3 Mahalanobis .......................36
3.2 二階段辨識法 ........................36
3.3 大分類 ..............................41
3.3.1 非監督式學習 ......................41
3.3.2 監督式學習 ........................44
3.4 細分類 ..............................46
3.4.1 K-NN ..............................46
3.4.2 類神經網路 ........................46
第4章 實驗結果 ..........................50
4.1 人臉資料庫 ..........................50
4.2 實驗分析 ............................52
第5章 結論與未來研究方向 ................61
5.1 總結 ................................61
5.2 未來工作 ............................63
參考文獻 ................................64
[1] Turk M. A. and Pentland P., ”Face Recognition Using Eigenfaces”, Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 586-591, 1991

[2] Belhumeur P., Hespanha J., and Kriegman D., ”Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection”, IEEE Trans, PAMI, 19(7): 711-720. 1997

[3] Yuela, P.C.; Dai, D.Q.; Feng, G.C., “Wavelet-based PCA for human face recognition”, Image Analysis and Interpretation, 1998 IEEE Southwest Symposium on, pp. 223 –228, 1998

[4] Lee, W.S.; Lee, H.J.; Chung, J.H., “Wavelet-based FLD for face recognition”, Circuits and Systems, 2000. Proceedings of the 43rd IEEE Midwest Symposium on, Volume: 2, pp. 734 –737, 2000

[5] Chengjun Liu; Wechsler, H., “A Gabor feature classifier for face recognition”, Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on, Volume: 2, pp. 270 –275, 2001

[6] Moghaddam, B.; Wahid, W.; Pentland, A., “Beyond eigenfaces: probabilistic matching for face recognition”, Automatic Face and Gesture Recognition, 1998. Proceedings. Third IEEE International Conference on, pp. 30 –35, 1998

[7] Moghaddam, B.; Pentland, A., “Probabilistic matching for face recognition”, Image Analysis and Interpretation, 1998 IEEE Southwest Symposium on, pp. 186 –191, 1998

[8] Meng Joo Er; Shiqian Wu; Juwei Lu; Hock Lye Toh., ” Face recognition with radial basis function (rbf) neural networks”, Neural Networks, IEEE Transactions on, Volume: 13 Issue: 3 , pp. 697 –710, 2002

[9] Tolba, A.S.; Abu-Rezq, A.N., “Combined classifiers for invariant face recognition”, Information Intelligence and Systems, 1999. Proceedings. 1999 International Conference on, pp. 350 –359, 1999

[10] Phiasai, T.; Arunrungrusmi, S.; Chamnongthai, K., “Face recognition system with PCA and moment invariant method”, Circuits and Systems, 2001. ISCAS 2001. The 2001 IEEE International Symposium on, Volume: 2, pp. 165 –168, 2001

[11] Yambor W., Draper B., and Beveridge J.R., “Analysis of PCA-based Face Recognition Algorithms: Eigenvector Selection and Distance Measure”, Second Workshop on Empirical Evaluation Methods in Computer Vision. 2000

[12] Zhujie and Y. L. Yu, “Face Recognition with Eigenfaces”, Proc. IEEE Conf. on Industrial Technology, pp. 434-438, 1994

[13] Martinez, A.M.; Kak, A.C., “PCA versus LDA”, Pattern Analysis and Machine Intelligence, IEEE Transactions on, Volume: 23 Issue: 2, pp. 228-233, 2001

[14] Zhao W., Krishnaswamy A., Chellappa R., Swets D., and Weng J., “Discriminant Analysis of Principle Components for Face Recognition”, 3rd International Conference on Automatic Face and Gesture Recognition, pp. 336-341, 1998

[15] Richard O. Duda, Peter E. Hart, David G. Stork, Pattern Classification, pp. 114-121, 2000

[16] R. Chellappa, C.L. Wilson and S. Sirohey, “Human and Machine Recognition of Faces, A Survey” proc. of the IEEE, Vol. 83, pp. 705-740, 1995.

[17] M. Turk and A. Pentland, “Eigenfaces for Recognition” Journal of Cognitive Neuroscience, Vol. 3, pp. 72-86, 1991.


[18] B. Moghaddam and A. Pentland, “Probabilistic Visual Learning for Object Detection” in Proc. International Conference on Computer Vision, Boston, MA, pp. 786-793, 1995.

[19] Tolba, A.S.; Abu-Rezq, A.N., “Combined classifiers for invariant face recognition” Information Intelligence and Systems, 1999. Proceedings. 1999 International Conference on, pp. 350-359, 1999

[20] Jain, A.K.; Duin, P.W.; Jianchang Mao, “Statistic pattern recognition: a review” Pattern Analysis and Machine Intelligence, IEEE Transactions on, Volume: 22 Issue: 1, pp. 4-37, 2000

[21] Scott E Umbaugh, Ph. D., Computer Vision and Image Processing: A Practical Approach Using CVIPtools, pp. 125-130, 1998

[22] Wendy S. Yambor, “Analysis of PCA-Based and Fisher Discriminant-Based Image Recognition Algorithms”, Master’s thesis, Colorado State University, 2000
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