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研究生： 鄭亦曾
Yi-Tseng Cheng
論文名稱： 結合局部區域與仿生特徵之年齡估測
Landmark Oriented Generalized Biologically Inspired Features for Age Estimation
指導教授： 徐繼聖
Gee-Sern Hsu
口試委員: 賴尚宏
Shang-Hong Lai
王鈺強
Yu-Chiang Wang
郭景明
Jing-Ming Guo
亞魯
ArulMurugan Ambikapathi
學位類別： 碩士
Master
系所名稱： 工程學院 - 機械工程系
Department of Mechanical Engineering
論文出版年： 2016
畢業學年度： 104
語文別： 中文
論文頁數： 51
中文關鍵詞： 年齡估測 、仿生特徵
外文關鍵詞： Age estimation, Bio-Inspired feature
相關次數： 點閱：195  下載：3 
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    • 本論文提出了Generalized Biologically Inspired Features (GBIFs)，和移動式分類視窗來做年齡估測。GBIF比BIF還要更有彈性，可以適應更多不同種類的影像擷取出臉部年齡相關特徵，BIF特徵被廣泛用於年齡估測特徵，但沒有論文去研究此特徵，本論文提出的GBIF除了可以在臉部局部區域可以做選擇，頻帯(Band)數量、相鄰取極值濾波後影像數目 和濾波器方向 都可以做調整；在分類的部分，先區分年齡群組再進行估測可降低錯誤率，這種分類法稱為分層式年齡估測(Hierarchical Age Estimation)，許多文獻都是使用此方法，但不同的文獻就有不同的年齡群組與不同的群組分界線，本論文提出移動式分類視窗，以定義出最佳的年齡群組分界線，同時討論了不同分層式分類架構，決定出最好的分層式分類架構，再搭配軟邊界回歸用於指標性的公開年齡資料庫，如FG-NET與MORPH作測試，實驗證明效能近似甚或優於近期文獻中所提之年齡估測方法。

    We propose the Generalized Biologically Inspired Features (GBIFs) and a moving segmentation scheme followed by soft boundary regression for age estimation. The GBIF is more advantageous than the Bio-Inspired Feature (BIF) for capturing age-related facial traits. The moving segmentation is proposed to better determine the age groups, leading to an improvement on the age estimation accuracy. Different from most approaches that segment the age groups in an ad-hoc way, the moving segmentation allows one to define age groups using the local minima in the misclassification rate across ages. The extraction of the GBIF depends on the partition of component regions defined by facial landmarks. In addition to the partition of component regions, we also study the appropriate age grouping and hierarchical classification, and determine the best configuration for age estimation. The proposed approach with the most appropriate settings outperforms most of the state of the art on two benchmarks, FG-NET and MORPH.

    摘要 III Abstract IV 誌謝 V 圖目錄 VIII 表目錄 IX 第一章　介紹 1 1.1 研究背景和動機 1 1.2 方法概述 2 1.3 論文貢獻 3 1.4 論文架構 3 第二章　相關文獻探討 4 2.1 臉部年齡估測評估規範 4 2.2常用的年齡特徵擷取方式 5 2.2.1 主動外觀模型(Active Appearance Models) 5 2.2.2 仿生特徵(Biologically Inspired Features) 8 2.3支持向量回歸 (Support Vector Regression, SVR) 13 2.4回歸式樹狀模型(Regressive Tree Structured Model) 14 2.5分層式年齡估測的相關文獻 16 第三章　特徵擷取與分類回歸 18 3.1 Generalized Biologically Inspired Feature 18 3.2 移動式分類視窗 20 3.3 分層式分類結構 22 3.4 軟邊界回歸 第四章　實驗設置與分析 4.1 標準資料庫介紹 4.1.1 FG-NET database介紹 4.1.2 MORPH database介紹 4.2 實驗樣本設置與前處理 4.3 移動式分類實驗 4.4 分層式分類架構比較 4.5 GBIF的參數比較 4.6 特徵響應研究實驗 4.7 軟邊界回歸實驗 4.8 文獻比較結果 第五章　結論與未來研究方向 參考文獻

    [1] Mu, G., Guo, G., Fu, Y., & Huang, T. S. (2009, June). Human age estimation using bio-inspired features. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on (pp. 112-119). IEEE.
    [2] K. Luu, K. Seshadri, M. Savvides, T. Bui, and C. Suen. Contourlet appearance model for facial age estimation. In IJCB, Oct 2011.
    [3] S. Kohli, S. Prakash, and P. Gupta. Hierarchical age estimation with dissimilarity-based classification. Neurocomputing, 120:164–176, 2013.
    [4] A. Lanitis, C. Draganova, and C. Christodoulou. Comparing different classifiers for automatic age estimation. IEEE, 34(1):621–628, Feb 2004.
    [5] P. Thukral, K. Mitra, and R. Chellappa. A hierarchical approach for human age estimation. In ICASSP,IEEE, pages 1529–1532, 2012.
    [6] A. Lanitis, C. Draganova, and C. Christodoulou. Comparing different classifiers for automatic age estimation. IEEE, 34(1):621–628, Feb 2004.
    [7] G. Guo, G. Mu, Y. Fu, C. Dyer, and T. Huang. A study on automatic age estimation using a large database. In IEEE, pages 1986–1991, Sept 2009.
    [8] F. Gao and H. Ai. Face age classification on consumer images with gabor feature and fuzzy lda method. LNCS, pages 132–141. 2009.
    [9] G. Guo and X. Wang. A study on human age estimation under facial expression changes. In IEEE,CVPR, pages 2547– 2553, 2012.
    [10] Chang, Kuang-Yu, Chu-Song Chen, and Yi-Ping Hung. "Ordinal hyperplanes ranker with cost sensitivities for age estimation." Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on. IEEE, 2011.
    [11] H. Han, C. Otto, and A. Jain. Age estimation from face images: Human vs. machine performance. In ICB, pages 1–8, 2013.
    [12] H. Han, C. Otto, X. Liu, and A. Jain. Demographic estimation from face images: Human vs. machine performance. Pattern Analysis and Machine Intelligence, IEEE, 37:1148– 1161, 2015.
    [13] Cootes, T. F., Edwards, G. J., & Taylor, C. J. (1998). Active appearance models. In Computer Vision—ECCV’98 (pp. 484-498). Springer Berlin Heidelberg.
    [14] A. Lanitis, C. Draganova, and C. Christodoulou, “Comparing Different Classifiers for Automatic Age Estimation,” IEEE Trans. Systems, Man, and Cybernetics Part B, vol. 34, no. 1, pp. 621-628, Feb. 2004.
    [15] Lu, Jiwen, and Yap-Peng Tan. "Ordinary preserving manifold analysis for human age and head pose estimation." Human-Machine Systems, IEEE Transactions on 43.2 (2013): 249-258.
    [16] T.F. Cootes, D. Cooper, C.J. Taylor, and J. Graham, “Active Shape Models—Their Training and Application,” Computer Vision and Image Understanding, vol. 61, no. 1, pp. 38-59, Jan. 1995.
    [17] M. Riesenhuber and T. Poggio, “Hierarchical Models of Object Recognition in Cortex,” Nature Neuroscience, vol. 2, no. 11, pp. 1019-1025, 1999.
    [18] T. Serre, L. Wolf, S. Bileschi, M. Riesenhuber, and T. Poggio, “Robust Object Recognition with Cortex-Like Mechanisms,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 3, pp. 411-426, Mar. 2007.
    [19] Geng, Xin, Chao Yin, and Zhi-Hua Zhou. "Facial age estimation by learning from label distributions." Pattern Analysis and Machine Intelligence, IEEE Transactions on 35.10 (2013): 2401-2412.
    [20] A. J. Smola and B. Scholkopf, “A Tutorial on Support Vector Regression”, September 30, 2003
    [21] The FG-NET Aging Database, http://www.fgnet.rsunit.com/, http://www-prima.inrialpes.fr/FGnet/, 2010
    [22] K. Ricanek and T. Tesafaye, “MORPH: A Longitudinal Image Database of Normal Adult Age-Progression,” Proc. IEEE Int’l Conf. Automatic Face and Gesture Recognition, pp. 341-345, 2-6 April 2006.
    [23] MORPH Face Database, http://faceaginggroup.com/, 2010.
    [24] P. Viola and M. Jones, ``Rapid object detection using a boosted cascade of simple features,'' in IEEE Proc. Int. Conf. Computer Vision and Pattern Recognition, vol. 1, p. 511–518, 2001.
    [25] G.S. Hsu, K.H.Chang & S.C. Huang, “Regressive Tree Structured Model for Facial Landmark Localization”. In Proceedings of the IEEE International Conference on Computer Vision (pp. 3855-3861).
    [26] Choi, S. E., Lee, Y. J., Lee, S. J., Park, K. R., & Kim, J. (2011). Age estimation using a hierarchical classifier based on global and local facial features. Pattern Recognition, 44(6), 1262-1281.
    [27] Luu, K., Ricanek, K., Bui, T. D., & Suen, C. Y. (2009, September). Age estimation using active appearance models and support vector machine regression. In Biometrics: Theory, Applications, and Systems, 2009. BTAS'09. IEEE 3rd International Conference on (pp. 1-5). IEEE.
    [28] Yun Fu, Guodong Guo, Thomas S. Huang, "Age Synthesis and Estimation via Faces: A Survey," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 11, pp. 1955-1976, November, 2010
    [29] X. Zhu, D. Ramanan. "Face detection, pose estimation and landmark localization in the wild",Computer Vision and Pattern Recognition (CVPR) Providence, Rhode Island, June 2012.
    [30] H. Rowley, S. Baluja, and T. Kanade. Neural network-based face detection. Pattern Analysis and Machine Intelligence, IEEE Trans., 20(1):23–38, 1998.
    [31] P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan.”Object detection with discriminatively trained partbased models.” IEEE TPAMI, 2009.

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