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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 羅仁權(Ren-Chyuan Luo) | |
| dc.contributor.author | Li-Wen Chang | en |
| dc.contributor.author | 張瓈文 | zh_TW |
| dc.date.accessioned | 2021-06-16T10:44:55Z | - |
| dc.date.available | 2018-08-14 | |
| dc.date.copyright | 2013-08-14 | |
| dc.date.issued | 2013 | |
| dc.date.submitted | 2013-08-12 | |
| dc.identifier.citation | [1] R. C. Luo, P. H. Lin, and L. W. Chang, “Confidence Fusion Based Emotion Recognition of Multiple Persons for Human-Robot Interaction,” IEEE International Conference on Intelligent Robots and Systems (IROS2012), October 7-12, 2012, Vilamoura, Algarve [Portugal].
[2] 林佩嫻(Pei-Hsien Lin),” 應用於人機互動之多人表情辨識與環境氛圍辨識系統,” 臺灣大學電機工程學研究所碩士學位論文, 2012. [3] Y. Fu and G. Guo, “Age Synthesis and Estimation via Faces: A Survey,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 32, no. 11, pp. 1955-1975, 2010. [4] Japanese smokers to face age test, http://news.bbc.co.uk/2/hi/asia-pacific/7395910.stm, 2008. [5] Y. Kwon and N. Lobo, “Age Classification from Facial Images,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 762-767, 1994. [6] Y. W. Chen, M. J. Han, K. T. Song and Y. L. Ho, “Image-Based Age-Group Classification Design Using Facial Features,” Proc. Conf. on System Science and Engineering, pp. 548 - 552, 2010. [7] K. Ueki, M. Sugiyama, Y. Ihara, and M. Fujita, “Multi-Race Age Estimation Based on the Combination of Multiple Classifiers,” 2011 First Asian Conf. on Pattern Recognition (ACPR), pp. 633 - 637, 2011. [8] A. Gunay, Vasif V. Nabiyev, “Automatic Age Classification with LBP,” International Symposium on Computer and Information Sciences (ISCIS), pp.1-4, 2008. [9] G. Guo, and X. Wang, “A Study on Human Age Estimation under Facial Expression Changes,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 2547 - 2553, 2012. [10] K. Luu, K. Seshadri, M. Savvides, T. D. Bui, and C. Y. Suen, “Contourlet Appearance Model for Facial Age Estimation,” 2011 International Joint Conference on Biometrics (IJCB), pp.1-8, 2011. [11] H. Fukai, H. Takimoto, Y. Mitsukura, and M. Fukumi, 'Age and gender estimation by using facial image,' 2010 11th IEEE International Workshop on Advanced Motion Control, pp.179-184, 21-24 March 2010. [12] N. Ramanathan and R. Chellappa, “Modeling Age Progression in Young Faces,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 387-394, 2006. [13] P. Moreno, A. Bernardino, J. Santos-Victor, “Gabor Parameter Selection for Local Feature Detection,” Proceedings of IBPRIA, 2005. [14] B. Zhang, S. Shan, X. Chen, and W. Gao, “Histogram of Gabor Phase Patterns (HGPP): A Novel Object Representation Approach for Face Recognition,” IEEE Transactions on Image Processing, 2007. [15] T. F. Cootes, G. J. Edwards, C. J. Taylor ,“Active Appearance Models “, IEEE Transactions on Pattern Analysis and Machine Intelligence, VOL. 23, NO. 6, JUNE 2001. [16] The FG-NET Aging Database: http://sting.cycollege.ac.cy/»alanitis/ fgnetaging/index.htm. [17] J. Hartigan, M. Wang, “A K-means clustering algorithm”, Applied Statistics 1979, 28:100-108. [18] P. Viola, M. Jones, “Robust Realtime Face Detection,” International Journal of Computer Vision, 2004. [19] R. C. Gonzalez, R. E. Woods, “Intensity Transformations and Spatial Filtering,” in Digital Image Processing, 3rd ed. New Jersey: Pearson, 2008, ch. 3, sec. 3, pp. 122–127. [20] C. Cortes and V. Vapnik, 'Support-Vector Networks,' Machine Learning, vol. 20, no. 3, pp. 273-297, September 1995. [21] N. Chinchor, “MUC-4 Evaluation Metrics,” in Proc. of the Fourth Message Understanding Conference, pp. 22–29, 1992. [22] C. J. van Rijsbergen, “Information Retrieval,” London:Butterworths, 1979. [23] K. Ricanek and T. Tesafaye, “MORPH: A longitudinal image database of normal adult age-progression,” in Proc. the 7th Int’l Conf. Automatic Face and Gesture Recognition, Southampton, UK, 2006, pp. 341–345. [24] Electronic Customer Relationship Management (ECRM), http://en.wikipedia.org/wiki/ECRM, 2010. [25] G. Guo, Y. Fu, C. Dyer, and T.S. Huang, “Image-Based Human Age Estimation by Manifold Learning and Locally Adjusted Robust Regression,” IEEE Trans. Image Processing, vol. 17, no. 7, pp. 1178-1188, July 2008. [26] 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. [27] N. Ramanathan and R. Chellappa, “Face Verification across Age Progression,” IEEE Trans. Image Processing, vol. 15, no. 11, pp. 3349-3361, Nov. 2006. [28] V. Blanz and T. Vetter, “A Morphable Model for the Synthesis of 3D Faces,” Proc. ACM SIGGRAPH, pp. 187-194, 1999. [29] Y. Fu, N. Zheng, J. Liu, and T. Zhang, “Facetransfer: A System Model of Facial Image Rendering,” Proc. IEEE Conf. Systems, Man, and Cybernetics, pp. 4528-4534, 2004. [30] M. Das and A.C. Loui, “Automatic Face-Based Image Grouping for Albuming,” Proc. IEEE Conf. Systems, Man, and Cybernetics, vol. 4, pp. 3726-3731, 2003. [31] R. C. Luo, T. T. Lin, M. C. Tsai, “Gender Classification Based on Multi-Classifiers Fusion for Human-Robot Interaction,” IEEE International Symposium on Industrial Electronics, Gdansk, Poland, 2011. [32] 林子達(Tzu-Ta Lin), ”人機互動透過身份辨識和驗證應用於智慧型服務機器人,” 臺灣大學電機工程學研究所碩士學位論文, 2011. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/61074 | - |
| dc.description.abstract | 電腦科技和資訊科學的發展日新月異，在這樣的趨勢之下，智慧型機器人的重要性逐漸被應用於各個層面，包括自動化工業、軍事國防、保全巡邏、居家照護、教育娛樂等。而以人為核心的社會環境當中，人與機器人的互動關係便成為相當受到重視的一環，因此我們致力於開發各種人類資訊的偵測系統落實於電腦或機器人上，經由擷取人類的各種靜態與動態資訊，使得電腦或機器人有良好的先前知識來達到人機互動的目的，例如一個先進的健康照護系統，可以根據患者年齡自動配置適當的虛擬護士，隨時監控病情給予照顧，同時帶來互動的樂趣；另一方面藉由年齡資訊的擷取，我們能提升保全監控的應用面，例如管制未成年人從事違法行為等以降低人力資源的支出。
本論文主題為開發人類臉部年齡辨識系統，並應用於人與機器互動。一開始先說明年齡辨識的應用範圍以及重要性，接著針對人臉特徵的選擇進行比較與測試，在本論文中我們決定選用全相貌影像作為判斷年齡的特徵依據，並且將年齡分成共七個群組，每十歲為一組，大於60歲則皆屬於同一群組。我們採用FG-NET和MORPH這兩個西方人臉資料庫，以支持向量機做學習，最後結合F-measure的分數以加權的方式決定辨識出的所屬年齡群組。本系統所得到的辨識結果優於人的肉眼主觀判斷，將此一結果以即時辨識的方式和電腦做互動，藉由人機介面的設計讓使用者簡單操作，達到人機互動的效果。 本論文所提出的演算法與程式架構皆於Windows 7作業系統上以C++程式語言開發，且依據辨識結果，將所有軟體硬體整合，預期應用在本實驗室自行開發的智慧型機器人上。 | zh_TW |
| dc.description.abstract | Since computer technology and information science change with each passing day, intelligent robots become more important in a variety of fields, such as industrial automation, military defense, security guard, in-home nurse, education and entertainment and so on. For this human-centered society, the interaction between human and robot is regarded as a significant part of the technological environment. Therefore, many scientists and researchers dedicated their time to develop all kinds of human information estimation systems so as to implement on computers and robots. Through the detection of static and dynamic human information, the purpose of human-machine interaction could be achieved according to the information.
There are many modern applications require the function of age estimation such as security control and surveillance monitoring, health care system and so on. In this study, we propose a method to classify human age using appearance images and apply it to the human-robot interactions. We first confirm that facial features based on craniology are not discriminative under the condition of seven age-groups classification. Next, our system is designed to have two stages. One is image preprocess stage; faces are detected and preprocessed. Our image database is from FG-NET and MORPH databases so that we have high degree of complexity in training dataset. Then images are trained by support vector machines (SVM). To have higher recognition rate, we train RBF (radial basis function) and linear kernel models at the same time, and decide the final results by F-measure based weighting policy. We also compare the age-group classification results with subjective questionnaires, and it demonstrates that the proposed system has better performance than human’s subjective estimation. For the purpose of human-machine interaction, we design a simple user interface to perform online age-group classification. The system can be applied on any computer or robot as long as it has a camera sensor. All the algorithms and software programs proposed in this thesis are implemented with C++ programming language in Windows 7 platform, and the integrated development environments are Microsoft Visual Studio. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T10:44:55Z (GMT). No. of bitstreams: 1 ntu-102-R00921008-1.pdf: 3421970 bytes, checksum: 1a6ebd95a333fe133090d6736ec366cf (MD5) Previous issue date: 2013 | en |
| dc.description.tableofcontents | 口試委員審定書………………………………………………………………………...#
誌謝……….. i 中文摘要……… ii Abstract……… iii Table of Contents v List of Figures vii List of Tables x Chapter 1 Introduction 1 1.1 Era of Robot 1 1.2 Human-Robot Interaction 3 1.3 Human Age Estimation 5 1.4 Organization 9 Chapter 2 Background Knowledge 11 2.1 Mainstream of Human Age Estimation 11 2.1.1 Cranio-facial Changes 11 2.1.2 Image Features and Descriptors 14 2.1.3 Appearance Images 16 2.2 Analysis for Feature Selection 17 2.2.1 Active Appearance Models (AAM) 18 2.2.2 K-Means Clustering 20 2.2.3 Experimental Results 22 Chapter 3 Face Detection and Preprocess 24 3.1 Face detection 24 3.2 Contrast Enhancement 28 Chapter 4 Machine Learning 30 4.1 Algorithm Types 30 4.1.1 Supervised Learning 30 4.1.2 Unsupervised Learning 31 4.2 Support Vector Machine (SVM) 31 Chapter 5 Human Age-group Classification 34 5.1 Procedure 34 5.2 F-measure Based Weighting Policy 35 5.2.1 Confusion Matrices 35 5.2.2 F-measure Based Weighting Policy 38 5.3 Experiments 39 5.3.1 Database 39 5.3.2 Results 41 5.4 Subjective Questionnaire Results 49 5.5 Discussion and Comparison 51 Chapter 6 Applications of Human Age Estimation 54 6.1 Related Works 54 6.1.1 Age Synthesis 55 6.1.2 Age Estimation 56 6.2 Hardware Architecture 59 6.3 Scenario 60 6.4 System Structure of Online Human Age-group Classification 61 6.5 Experimental Results 63 6.6 Gender and Age-group Recognition for Human-Robot Interaction 67 6.6.1 Methodologies 68 6.6.2 Scenario 69 6.6.3 Applications 70 Chapter 7 Conclusions and Contributions 72 7.1 Conclusions 72 7.2 Contributions 73 Chapter 8 Future Works 74 References 75 VITA 78 | |
| dc.language.iso | en | |
| dc.subject | 人臉偵測 | zh_TW |
| dc.subject | 支持向量機 | zh_TW |
| dc.subject | 臉部年齡辨識 | zh_TW |
| dc.subject | F評量 | zh_TW |
| dc.subject | 人機互動 | zh_TW |
| dc.subject | Human-Computer Interactions | en |
| dc.subject | Support Vector Machines (SVM) | en |
| dc.subject | Facial Age Estimation | en |
| dc.subject | Face Detection | en |
| dc.subject | F measure | en |
| dc.title | 臉部年齡辨識系統應用於人與機器人互動 | zh_TW |
| dc.title | Age Estimation Using Appearance Images for Human-Robot Interaction | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 101-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 張帆人(Fan-Ren Chang),黃國勝(Kao-Shing Hwang) | |
| dc.subject.keyword | 人臉偵測,支持向量機,臉部年齡辨識,F評量,人機互動, | zh_TW |
| dc.subject.keyword | Face Detection,Support Vector Machines (SVM),Facial Age Estimation,F measure,Human-Computer Interactions, | en |
| dc.relation.page | 78 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2013-08-13 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| 顯示於系所單位： | 電機工程學系 | |
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