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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 吳家麟(Ja-Ling Wu) | |
dc.contributor.author | Tsung-Hao Ku | en |
dc.contributor.author | 顧宗浩 | zh_TW |
dc.date.accessioned | 2021-06-16T13:23:07Z | - |
dc.date.available | 2013-08-20 | |
dc.date.copyright | 2013-08-20 | |
dc.date.issued | 2013 | |
dc.date.submitted | 2013-07-24 | |
dc.identifier.citation | [1] H. Chang, D.-Y. Yeung, and Y. Xiong. Super-resolution through neighbor embedding.
In Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on, volume 1, pages I–275 – I–282 Vol.1, june-2 july 2004. [2] R. A. Fisher. The use of multiple measurements in taxonomic problems. Annals of Eugenics, 2:179–188, 1936. [3] A. Griffin and D. Viehland. Demographic factors in assessing perceived risk in online shopping. In Proceedings of the 13th International Conference on Electronic Commerce, ICEC ’11, pages 9:1–9:6, New York, NY, USA, 2012. ACM. [4] S. Gutta, H. Wechsler, and P. Phillips. Gender and ethnic classification of face images. In Automatic Face and Gesture Recognition, 1998. Proceedings. Third IEEE International Conference on, pages 194–199, 1998. [5] A. Hadid and M. Pietikainen:. Demographic classification from face videos using manifold learning. Neurocomputing, 100(0):197 – 205, 2013. [6] X. He and P. Niyogi. Locality preserving projections. In In Advances in Neural Information Processing Systems 16. MIT Press, 2003. [7] B. J. Jansen and L. Solomon. Gender demographic targeting in sponsored search. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’10, pages 831–840, New York, NY, USA, 2010. [8] S. Khan, M. Nazir, S. Akram, and N. Riaz. Gender classification using image processing techniques: A survey. In Multitopic Conference (INMIC), 2011 IEEE 14th International, pages 25–30, 2011. [9] B. Li, H. Chang, S. Shan, and X. Chen. Low-resolution face recognition via coupled locality preserving mappings. Signal Processing Letters, IEEE, 17(1):20 –23, jan. 2010. [10] B. Moghaddam and M.-H. Yang. Learning gender with support faces. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(5):707–711, 2002. [11] K. Pearson. On lines and planes of closest fit to systems of points in space. Philosophical Magazine, 11:559–572, 1901. [12] P. Phillips, H. Moon, S. Rizvi, and P. Rauss. The feret evaluation methodology for face-recognition algorithms. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 22(10):1090–1104, Oct. [13] R. Roscher, F. Schindler, and W. Forstner. High dimensional correspondences from low dimensional manifolds: an empirical comparison of graph-based dimensionality reduction algorithms. In Proceedings of the 2010 international conference on Computer vision - Volume part II, ACCV’10, pages 334–343. Springer-Verlag, 2011. [14] S. T. Roweis and L. K. Saul. Nonlinear dimensionality reduction by locally linear embedding. SCIENCE, 290:2323–2326, 2000. [15] D. D. L. Sebastian H. Seung. The manifold ways of perception science. Science, 290(0):2268 – 2269, 2000. [16] C. Shan. Learning local binary patterns for gender classification on real-world face images. Pattern Recognition Letters, 33(4):431 – 437, 2012. Intelligent Multimedia Interactivity. [17] C. Shan. Learning local binary patterns for gender classification on real-world face images. Pattern Recogn. Lett., 33(4):431–437, Mar. 2012. [18] U. Tariq, Y. Hu, and T. Huang. Gender and ethnicity identification from silhouetted face profiles. In Image Processing (ICIP), 2009 16th IEEE International Conference on, pages 2441–2444, 2009. [19] J. Tenenbaum. A global geometric framework for nonlinear dimensionality reduction. Science, 290:2319–2323, 2000. [20] C. Wang and S. Mahadevan. A general framework for manifold alignment, 2009. [21] X. Wang and X. Tang. Hallucinating face by eigentransformation. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 35(3):425 – 434, aug. 2005. [22] Z. Yang and H. Ai. Demographic classification with local binary patterns. In Proceedings of the 2007 international conference on Advances in Biometrics, ICB’07, pages 464–473, Berlin, Heidelberg, 2007. Springer-Verlag. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/62011 | - |
dc.description.abstract | 近年來,使用人臉影像進行人口特性的分類,性別、年齡與種族,
是在多媒體研究裡一項熱門的主題。它在使用者互動系統,或是市場 策略中都扮演著重要的角色。但是高解析度的人臉影像並非永遠唾手 可得,舉例來說,若人臉太遠離攝影機的鏡頭或是攝影的硬體限制都 會產生低解析度的人臉影像。在目前的文獻中,我們是第一位進行處 理高解析度與低解析度影像的連結,在性別、年齡與種族分類問題。 在這項研究中,我們推出了一項系統,能夠快速地處理此類問題,而 不是直觀地使用超解析度(Super Resolution)去處理低解析度影像。 我們使用了流型對齊(Manifold Alignment)直接連接高與低解析度影 像。在我們設計的實驗中,能夠證明我們的方法不但在準確率上高於 傳統的演算法,而且在速度上也能夠超越。 | zh_TW |
dc.description.abstract | Image-based demographic classification from human faces has been an
active topic of multimedia research over the past few years because of its fundamental role in creating a wide context of applications, such as user-aware interaction and strategic marketing planning. However, facial images of high resolution are not always available. For example, due to the hardware limitations of consumer surveillance cameras or people standing at a distance with their faces in small size, it is possible to cause the so-called Low Resolution (LR) problem. To the best of our knowledge, this work is the first in the literature to study the demographic classification problem with a focus on the connection between high resolution (HR) and LR images. Instead of using Super-Resolution (SR) as a preprocessing step, intuitively, to upsample LR images first, in this work, we developed an efficient framework to identify the demographic information, including age and gender, by employing the Manifold alignment techniques to connect the LR and the HR image spaces directly. In the experiments, the proposed approach is evaluated on a public dataset FERET and compared with the baseline algorithm. The results showed that our “one-step” framework can not only achieve better classification performances but also better time efficiency, which implies the proposed approach is more suitable for practical usage. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T13:23:07Z (GMT). No. of bitstreams: 1 ntu-102-R00922028-1.pdf: 4678483 bytes, checksum: 56e7e07da0897d637dd0012d5306559c (MD5) Previous issue date: 2013 | en |
dc.description.tableofcontents | 摘要ii
Abstract iii 1 Introduction 1 2 Related Work 4 2.1 Demographic Classification . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2 Underlying Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2.1 Manifold Learning . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2.2 Manifold Alignment . . . . . . . . . . . . . . . . . . . . . . . . 5 3 Manifold Alignment Based Classification System 7 3.1 The Overview of Manifold Alignment . . . . . . . . . . . . . . . . . . . 9 3.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.3 Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.3.1 Objective Function . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.3.2 Optimal Solution . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.4 Efficient Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 4 Experiment of Results 15 4.1 Experimental Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 4.1.1 The Baseline Algorithm . . . . . . . . . . . . . . . . . . . . . . 15 4.1.2 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 4.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.2.1 Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.2.2 The influence of correspondence weight . . . . . . . . . . . . . . 20 4.2.3 Visualization of Manifold Alignment . . . . . . . . . . . . . . . 20 4.2.4 Execution Time . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 5 Conclusion 23 Bibliography 24 | |
dc.language.iso | en | |
dc.title | 使用低解析度人臉影像進行性別、年齡與種族分類 | zh_TW |
dc.title | Demographic Classification based on Low-Resolution
Facial Images | en |
dc.type | Thesis | |
dc.date.schoolyear | 101-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 張智星(Jyh-Shing Jang),許秋婷(Chiou-Ting Hsu),鄭文皇(Wen-Huang Cheng) | |
dc.subject.keyword | 流行對齊,超解析度,性別分類,年齡分類,種族分類, | zh_TW |
dc.subject.keyword | Manifold Alignment,Super-Resolution,Gender Classification,Age Classification,Race Classification., | en |
dc.relation.page | 26 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2013-07-24 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
顯示於系所單位: | 資訊工程學系 |
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