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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 丁建均 | |
dc.contributor.author | Yu-Hsuan Tsai | en |
dc.contributor.author | 蔡宇軒 | zh_TW |
dc.date.accessioned | 2021-06-17T01:45:26Z | - |
dc.date.available | 2020-08-01 | |
dc.date.copyright | 2017-08-01 | |
dc.date.issued | 2017 | |
dc.date.submitted | 2017-07-27 | |
dc.identifier.citation | A. Review Paper
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Lee, “A robust face detection method based on skin color and edges,” Journal of Information Processing Systems, vol. 9, issue 1, pp. 141-156, 2013. [12] Y. Ban, S. K. Kim, S. Kim, K. A. Toh, and S. Lee, “Face detection based on skin color likelihood,” Pattern Recognition, vol. 47, issue 4, pp. 1573-1585, 2014. [13] K. W. Wong, K. M. Lam, and W. C. Siu, “A robust scheme for live detection of human faces in color images,” Signal Processing: Image Communication, pp.103-114, 2003. [14] Seshadrinathan, Manoj and B. A. Jezekiel, “Pose invariant face detection,” Video/Image Processing and Multimedia Communications, 4th EURASIP Conference focused on. Vol. 1, pp. 405-410, 2003. [15] HSV color model, available in http://blog.ibireme.com/wp-content/uploads/2013/08/HSV_color_solid_cylinder_alpha_lowgamma.png [16] S. E1. Kaddouhi, A. Saaidi, and M. 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Background Knowledge [22] Lab color model, available in https://s-media-cache-ak0.pinimg.com/originals/63/c8/9a/63c89aba0ed994edcfce462b2a4b2b6b.jpg [23] M. Y. Liu, O. Tuzel, S. Ramalingam, and R. Chellappa, “Entropy rate superpixel segmentation,” Computer Vision and Pattern Recognition, pp. 2097-2104, 2011. [24] C. W. Hsu, C. C. Chang, and C. J. Lin, “A practical guide to support vector classification,” 2003. [25] C. C. Chang and C. J. Lin, “LIBSVM: A library for support vector machines,” ACM Trans. Intelligent Systems and Technology, vol. 2, issue 3, article 27, 2011. [26] I. Jolliffe, “Principal component analysis,” John Wiley & Sons, Ltd, 2002. [27] P. P. Paul and M. Gavrilova, “PCA based geometric modeling for automatic face detection,” Computational Science and Its Applications, IEEE, pp. 33-38, 2011. [28] N. Otsui, “A threshold selection method from gray-level histograms,” IEEE transactions on systems, man, and cybernetics 9.1, pp. 62-66, 1979 [29] H. Y. Ko and J. J. Ding, “Adaptive growing and merging algorithm for image segmentation,” Signal and Information Processing Association Annual Summit and Conference, pp. 1-10, 2016. C. Databases [30] Our dataset, available in https://drive.google.com/file/d/0B-C-7fw2RhyrdFZFU2Z2akpIMW8/view?usp=sharing [31] European Conference on Visual Perception in Utrecht, available in http://pics.stir.ac.uk/2D_face_sets.htm, 2008. [32] C. E. Thomaz and G. A. Giraldi, “A new ranking method for principal components analysis and its application to face image analysis,” Image and Vision Computing, vol. 28, no. 6, pp. 902-913, June. 2010. [33] N. Gourier, D. Hall, and J. L. Crowley, “Estimating Face Orientation from Robust Detection of Salient Facial Features,” Proceedings of Pointing, ICPR, International Workshop on Visual Observation of Deictic Gestures, Cambridge, UK, 2004. [34] A. Gallagher and T. Chen, “Understanding Groups of Images of People,” IEEE Conference on Computer Vision and Pattern Recognition, 2009. [35] R. Frischholz, Bao face database at the face detection homepage, available in http://www.facedetection.com. D. Performance Measurement [36] D. M. Powers, “Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation,” Journal of Machine Learning Technologies, vol. 2, issue 1, pp. 37–63, 2011. E. Compared algorithms [37] Y. T. Pai, S. J. Ruan, M. C. Shie, and Y. C Liu, “A simple and accurate color face detection algorithm in complex background,” Multimedia and Expo, International Conference on. IEEE, pp. 1545-1548, 2006. [38] M. J. Marin-Jimenez, A. Zisserman, and V. Ferrari, “Here's looking at you, kid, Detecting people looking at each other in videos,” British Machine Vision Conference, vol. 5, pp. 1-12, 2011. [39] H. Cevikalp and B. Triggs, “Efficient object detection using cascades of nearest convex model classifiers,” Computer Vision and Pattern Recognition, pp. 3138-3145, 2012. [40] X. Zhu and D. Ramanan, “Face detection, pose estimation, and landmark localization in the wild,” Computer Vision and Pattern Recognition, pp. 2879-2886, 2012. [41] G. Ghiasi and C. Fowlkes, “Occlusion coherence: Localizing occluded faces with a hierarchical deformable part model,” IEEE Conf. Computer Vision and Pattern Recognition, pp. 2385-2392, 2014. [42] S. Liao, A. K. Jain, and S. Z. Li, “A fast and accurate unconstrained face detector,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, issue 2, pp. 211-223, 2016. [43] J. Chen, Y. Deng, G. Bai, and G. Su, “Face image quality assessment based on learning to rank,” IEEE Signal Processing Lett. vol. 22, issue 1, pp. 90-94, 2014. [44] Face++, available in http://www.faceplusplus.com. 2015. [45] R. L. Hsu, M. M. Abdel, and A. K. Jain, “Face detection in color images.” IEEE transactions on pattern analysis and machine intelligence. 24.5, pp. 696-706, 2002. [46] Z. X. Chen, C. Y. Liu, F. L. Chang, and X. Z. Han, “Fast Face Detection Algorithm Based on Improved Skin-Color Model.” Arabian Journal for Science and Engineering, vol. 38, no. 3, pp. 629-635, 2013. [47] C. Katsimerou, A. J. Redi, and I. Heynderickx, “Face Detection in Intelligent Ambiences with Colored Illumination.” Advances in Intelligent Systems and Computing, vol. 219, pp. 195-204, 2013. [48] S. Yadav and N. Nain, “Fast face detection based on skin segmentation and facial features.” Signal-Image Technology & Internet-Based Systems, pp. 663-668, 2015. [49] S. Yadav and N. Nain, “A novel approach for face detection using hybrid skin color model.” Journal of Reliable Intelligent Environments, pp. 145-158, 2016. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/67710 | - |
dc.description.abstract | 人臉偵測在很多應用中扮演著重要的角色,越來越多的研究及方法再解決人 臉偵測的問題。人臉偵測的最終結果即是自動分析一張輸入影像是否含有人臉的 存在,如果有的話即輸出人臉所在的座標,標示出人臉的位置。
近年來,許多研究嘗試改進經典的人臉偵測方法”Viola&Jones (Adaboost)”使這 個原始只能偵測正臉的演算法可以偵測多角度人臉,到目前為止,多角度的偵測 是一個還未被完美解決的問題,在這篇文章當中,我們提出了一個基於 Adaboost 正臉偵測器但卻可以偵測多角度人臉的方法,在不改變經典架構的前提下使其能 解決多角度的問題。在此方法中包含了以下的技術,膚色切割、超像素使我們得 到可能為人臉的候選區域,藉由角度補償及精確修正可以將被旋轉過的人臉轉回 正面,至於側臉的部分使用對稱延伸的方法,將一個側臉左右顛倒後和原始側臉 結合,創造、延伸出一個相似於真正人臉的正臉,經由這些處理後,多角度的人 臉都可以使用單一一個 Adaboost 的正臉偵測器來做偵測,不需要訓練別的角度偵 測器或是改變 Adaboost 的演算法就可以有相等甚至更好的效果,使 Adaboost 有更 完好的效能。 | zh_TW |
dc.description.abstract | Face detection plays an important role in many computer vision applications and has drawn significant research attention nowadays. The objective of face detection is to analyze whether the image contains face or not, and if it does, output the location of the bounding box for each face.
In recent years, many researches attempt to extend the well-established Viola & Jones (Adaboost) face detection algorithm to suitable for multi-view face detection. Until now, it is a challenge to detect in-plane, rotated, and out-of-plane face simultaneously. In this thesis, a very robust multi-view face detection algorithm is proposed. Although it is essentially a frontal face detector, it can well detect rotated, in-plane, and out-of-plane face without rotated training faces. First, several techniques, including the skin filter and entropy rate superpixel (ERS) are applied to obtain face candidate regions. Then, angle compensation and refinement are applied to improve the accuracy of face detection in in-plane case. Moreover, to find the out-of-plane face, one can apply the symmetry extension technique, i.e., extending the face candidate with its flipping version to create a face that is similar to the frontal one. With it, even if there are no training data for out-of-plane face, one can successfully detect the face in the out-of-plane case. Simulations on the FEI, Pointing'04, Bao, Group, Utrecht, and our dataset show that the proposed algorithm is effective and outperforms state-of-the-art face detection approaches. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T01:45:26Z (GMT). No. of bitstreams: 1 ntu-106-R04942105-1.pdf: 5851562 bytes, checksum: 81931bbe7d70c27300f4d4b76a6bc278 (MD5) Previous issue date: 2017 | en |
dc.description.tableofcontents | 口試委員會審定書 #
誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vi LIST OF TABLES x Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Multi-view Face Detection 1 Chapter 2 Review of Some Common Face Detection Algorithms 4 2.1 Haar-like Features and Adaboost Algorithm 4 2.2 Face Detection Based on Skin Color and Edges 7 2.3 Face Detection Method Based on Corner Points 10 2.4 Deformable Models for Multi-view Face Detection 14 2.5 Multi-view Face Detection Using Deep Convolutional Neural Networks 16 2.6 Aggregate Channel Features for Multi-view Face Detection 19 Chapter 3 Background Knowledge 22 3.1 Lab Color Space 22 3.2 Entropy Ratio Superpixel Segmentation 23 3.3 Support Vector Machine 26 3.4 Principal Component Analysis 29 3.5 Otsu Thresholding 30 Chapter 4 Overview of the Proposed Algorithm 32 Chapter 5 Proposed Multi-view Face Detection 34 5.1 Skin Filter 34 5.2 Superpixel Based Face Candidate 35 5.3 Frontal Face Detection 37 5.4 In-Plane Rotation Detection 39 5.4.1 Angle Compensation 39 5.4.2 Refinement of Angle Compensation 40 5.5 Out-of-Plane Rotation Detection 41 5.5.1 Symmetry Extension 41 5.6 Post Processing 45 5.6.1 Validation 45 5.6.2 Non-Maximum Suppression 46 5.7 Further Detection 47 Chapter 6 Simulation Result 49 6.1 Databases 49 6.2 Performance Measurement 51 6.3 Analysis 65 Chapter 7 Conclusion and Future Work 74 7.1 Conclusion 74 7.2 Future Work 74 REFERENCE 76 PUBLICATION 82 | |
dc.language.iso | en | |
dc.title | 多角度彩色人臉偵測 | zh_TW |
dc.title | In-Plane and Out-of-Plane Color Face Detection | en |
dc.type | Thesis | |
dc.date.schoolyear | 105-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 許文良,王家慶,張榮吉 | |
dc.subject.keyword | 人臉偵測,多角度,對稱延伸,超像素, | zh_TW |
dc.subject.keyword | face detection,in-plane rotation,out-of-plane rotation,symmetry extension,superpixel based face candidate, | en |
dc.relation.page | 82 | |
dc.identifier.doi | 10.6342/NTU201702053 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2017-07-27 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
顯示於系所單位: | 電信工程學研究所 |
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