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研究生:謝青穎
研究生(外文):Ching-ying Hsieh
論文名稱:臉部年齡評估之研究
論文名稱(外文):A study of age estimation based on facial images
指導教授:李建樹李建樹引用關係
指導教授(外文):Jiann-Shu Lee
學位類別:碩士
校院名稱:國立臺南大學
系所名稱:資訊工程學系碩士班
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:2012
畢業學年度:100
語文別:中文
論文頁數:38
中文關鍵詞:內嵌稀疏表示之廻歸法年齡評估稀疏表示植基於外觀老化傾向分類法
外文關鍵詞:Sparse-Representation Embedded RegressionAge EstimationSparse RepresentationAppearance-Aging-Inclination Based Classificatio
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近年來,臉部年齡資訊在人機互動(Human Computer Interaction,HCI)、使用者介面(User Interfaces)和生物識別技術(Biometrics)等應用上越來越重要,使得臉部年齡評估的相關研究也漸漸受到關注。但臉部年齡受遺傳、飲食和生活方式等因素的影響,要作出正確判斷並不容易,本研究提出一個新的評估方法,稱「內嵌稀疏表示之廻歸法」(Sparse-representation Embedded Regression),簡稱SRER,來做臉部年齡評估。本論文另外導入了Bagging演算法與植基於外觀老化傾向分類法(Appearance-aging-inclination Based Classification,AAIBC)來改善年齡評估的精確性。根據研究結果顯示,本論文所提方法能夠比現有大多數方法得出更精準的臉部年齡估測結果。
Recently, because of the information of facial age becomes significant in human computer interaction (HCI) and user interface designs, researches of face age estimation get a lot of attention. However, it is very difficult to accurately predict age from a face image due to the age information is often affected by many factors such as hereditary, diet, lifestyle etc. Therefore, accurate assessment of facial age is a challenging issue. In this thesis, we propose a new age estimation method, called Sparse-Representation Embedded Regression (SRER), to estimate the facial age. We adopt Bagging and Appearance-Aging-Inclination Based Classification (AAIBC) to improve the accuracy. The experimental results show that our method can outperform the state-of-the-art age estimation methods.
【目錄】
【摘要】 I
【ABSTRACT】 II
【誌謝】 III
【目錄】 IV
【表目錄】 V
【圖目錄】 VI
第1章 序論 1
1.1 動機與目的 1
1.2 論文架構 2
1.3 系統架構 3
第2章 文獻探討 5
第3章 研究方法 8
3.1 前處理 8
3.2 內嵌稀疏表示之廻歸法(SRER) 10
3.2.1 稀疏表示(Sparse Representation) [16] 10
3.2.2 Kernel-Based Linear Regression 14
3.3 BAGGING [18] 17
3.4 植基於外觀老化傾向分類法(AAIBC) 21
第4章 實驗結果 23
4.1 實驗環境 23
4.2 實驗一 25
4.3 實驗二 26
4.4 實驗三 28
4.5 實驗四 31
4.6 實驗五 32
4.7 實驗六 34
第5章 結論與未來展望 36
5.1 結論 36
5.2 未來展望 36
【參考文獻】 37
[1]The FG-NET Aging database http://www.fgnet.rsunit.com/.
[2]X. Geng, Z. H. Zhou, Y. Zhang, G. Li, and H. Dai, “Learning from Facial Aging Patterns for Automatic Age Estimation,” Proc. ACM Conf. on Multimedia, 2006.
[3]X. Geng, K. Smith-Miles, and Z.-H. Zhou, “Facial age estimation by nonlinear aging pattern subspace,” ACM international conference on Multimedia, pages 721-724, 2008.
[4]G. Guo, Y. Fu, T.S. Huang, and C. Dyer. “Locally adjusted robust regression for human age estimation,” IEEE Workshop on WACV, 2008.
[5]S. Yan, H. Wang, X. Tang and T. S. Huang, “Learning Auto-Structured Regressor from Uncertain Nonnegative Labels,” IEEE International Conf. on Computer Vision, 2007.
[6]Y. Zhang and D. Y. Yeung, “Multi-task warped gaussian process for personalized age estimation,” IEEE conf. on CVPR, 2010.
[7]C. Bauckhage, A. Jahanbekam and C.Thurau, “Age Recognition in the Wild,” International Conf. on Pattern Recognition (ICPR), 2010.
[8]X. Geng and K. Smith-Miles, “Facial age estimation by multilinear subspace analysis,” IEEE International Conference on Speech and Signal Processing(ICASSP), 2009.
[9]X. Zhuang, X. Zhou, Mark Hasegawa-Johnson, and T. Huang, “Face age estimation using patch-based hidden Markov model supervectors,” International Conf. on Pattern Recognition (ICPR), 2008.
[10]J. Lu and Y. P. Tan, “Ordinary preserving manifold analysis for human age estimation,” IEEE Computer Society Conf. on Computer Vision and Pattern Recognition Workshops (CVPRW), 2010.
[11]J. Suo, T. Wu, S. Zhu, S. Shan, X. Chen and W. Gao, “Design sparse features for age estimation using hierarchical face model,” IEEE International Conf. on Automatic Face & Gesture Recognition, 2008.
[12]G. Guo, G. Mu, Y. Fu and T.S. Huang, “Human age estimation using bio-inspired features,” IEEE International Conf. on CVPR, 2009.
[13]J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry and Y. Ma, “Robust Face Recognition via Sparse Representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 31, no. 2, February 2009.
[14]M. Zhou, H. Chen, J. Paisley, L. Ren, L. Li, Z. Xing, D. Dunson, G. Sapiro and L. Carin, “Nonparametric Bayesian Dictionary Learning for Analysis of Noisy and Incomplete Images,” IEEE Transactions on Image Processing, Vol. 21, no. 1, pages 130-144, 2012.
[15]Eye Detection http://yushiqi.cn/research/eyedetection/
[16]Y. Ji, T. Lin and H. Zha, “Mahalanobis Distance Based Non-negative Sparse Representation for Face Recognition,” IEEE International Conf. on Machine Learning and Applications, 2009.
[17]Machine-Learning(COMP-652) http://www.cs.mcgill.ca/~dprecup/courses/ml.html
[18]Richard O. Duda, Peter E. Hart and David G. Stork, “Pattern Classification,” 2nd ed., 2000.
[19]S. Yan, H. Wang, Y. Fu, J. Yan, X. Tang, and T. S. Huang, “Synchronized submanifold embedding for person-independent pose estimation and beyond,” IEEE Trans. on Image Processing., vol. 18, no. 1, pp. 202–210, Jan. 2009.
[20]K. Luo, K. Chen, Y. Chen and C. Hsu, “Facial Age Estimation Based On Person-Independent Nonnegative Matrix Factorization,” Conf. on CVGIP, 2011.
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