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題名 基於近紅外線影像之年齡層估算機制
A mechanism for age classification using near-infrared images
作者 林言翰
Lin, Yan-Han
貢獻者 郭耀煌<br>廖文宏
Kuo, Yau-Hwang<br>Liao, Wen-Hung
林言翰
Lin, Yan-Han
關鍵詞 年齡估計
近紅外線影像
局部二值模式
RealSense攝影機
Age estimation
Near infrared
Local Binary Patterns
RealSense camera
日期 2017
上傳時間 13-九月-2017 14:47:33 (UTC+8)
摘要 近紅外線影像由於其物理特性與成像方式，其紋理細節都有發散模糊不清的現象，對於以紋理為主要特徵的年齡辨識問題而言更具挑戰。本論文主要目的是以近紅外線人臉影像為基礎，找出對近紅外線年齡特徵有最佳描述力的特徵描述子，辨識近紅外線影像中被拍攝者的年齡區間，建構整個年齡層估算機制。
相關研究一部分關注可見光年齡辨識，另一部分則聚焦在近紅外線人臉辨識，目前還沒有近紅外線年齡辨識的相關文獻能參考，如何從接近的研究領域找尋是適當的演算法是本研究遇到的第一個挑戰。在資料庫的部分，FGNET和MORPH常被用於可見光的年齡辨識議題； PolyU和LDHF則用於近紅外線人臉辨識相關研究，在目前沒有近紅外線年齡資料庫的情況，本研究自建RSNIR（Intel RealSense Near-Infrared Age Database），因此如何標準化拍攝環境流程、蒐集穩定的近紅外線影像是本研究面臨的第二個挑戰。
區域性特徵擷取方法的關鍵在於特徵描述子的描述力。本研究以LBP（Local Binary Patterns）為基礎，探討LBP在內的24個特徵描述子，最後實驗測試各個描述子在RSNIR的辨識率，結果發現基本型Fuzzy LBP和擴充型RILBP對近紅外線年齡特徵有最佳描述力。在空間譜子區塊（patch）設計部分，以3x3切割子區塊數的辨識效果最好，反應出其與影像校正時的人臉影像空間定義方式有關。
參考文獻 [1] Burnay, S. G., T. L. Williams, and C. H. Jones, eds. Applications of thermal imaging. CRC Press, 1988.
[2] 薛玉利. 基于 Gabor 变换的特征提取及其应用. MS thesis. 山东大学, 2007.
[3] Ojala, Timo, Matti Pietikainen, and Topi Maenpaa. "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns." Pattern Analysis and Machine Intelligence, IEEE Transactions on 24.7 （2002）: 971-987.
[4] Tan, Xiaoyang, and Bill Triggs. "Enhanced local texture feature sets for face recognition under difficult lighting conditions." Analysis and Modeling of Faces and Gestures. Springer Berlin Heidelberg, 2007. 168-182.
[5] Liao, Wen-Hung. "Region description using extended local ternary patterns. "Pattern Recognition （ICPR）, 2010 20th International Conference on. IEEE, 2010.
[6] Liao, Wen-Hung. "Commensurate dimensionality reduction for extended local ternary patterns." Pattern Recognition （ICPR）, 2012 21st International Conference on. IEEE, 2012.
[7] Altman, Naomi S. "An introduction to kernel and nearest-neighbor nonparametric regression." The American Statistician 46.3 （1992）: 175-185.
[8] Guo, Guodong, Stan Z. Li, and Kapluk Chan. "Face recognition by support vector machines." Automatic Face and Gesture Recognition, 2000. Proceedings. Fourth IEEE International Conference on. IEEE, 2000.
[9] Burges, Christopher JC. "A tutorial on support vector machines for pattern recognition." Data mining and knowledge discovery 2.2 （1998）: 121-167.
[10] The FG-NET Aging Database, http://www.fgnet.rsunit.com/, http://www-prima.inrialpes.fr/FGnet/, 2010.
[11] Ricanek Jr, Karl, and Tamirat Tesafaye. "Morph: A longitudinal image database of normal adult age-progression." Automatic Face and Gesture Recognition, 2006. FGR 2006. 7th International Conference on. IEEE, 2006.
[12] MORPH Face Database, http://faceaginggroup.com/, 2010.
[13] PolyU NIR Face Database, http://www4.comp.polyu.edu.hk/~biometrics/polyudb_face.htm, 2010.
[14] Hsu, Rein-Lien, Mohamed Abdel-Mottaleb, and Anil K. Jain. "Face detection in color images." Pattern Analysis and Machine Intelligence, IEEE Transactions on 24.5 （2002）: 696-706.
[15] Yang, Ming-Hsuan, and Narendra Ahuja. "Detecting human faces in color images." Image Processing, 1998. ICIP 98. Proceedings. 1998 International Conference on. Vol. 1. IEEE, 1998.
[16] Viola, Paul, and Michael Jones. "Rapid object detection using a boosted cascade of simple features." Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on. Vol. 1. IEEE, 2001.
[17] Lienhart, Rainer, and Jochen Maydt. "An extended set of haar-like features for rapid object detection." Image Processing. 2002. Proceedings. 2002 International Conference on. Vol. 1. IEEE, 2002.
[18] Liao, Shengcai, et al. "Learning multi-scale block local binary patterns for face recognition." Advances in Biometrics. Springer Berlin Heidelberg, 2007. 828-837.
[19] Fu, Yun, Guodong Guo, and Thomas S. Huang. "Age synthesis and estimation via faces: A survey." Pattern Analysis and Machine Intelligence, IEEE Transactions on 32.11 （2010）: 1955-1976.
[20] Jabid, Taskeed, Md Hasanul Kabir, and Oksam Chae. "Local directional pattern （LDP） for face recognition." 2010 Digest of Technical Papers International Conference on Consumer Electronics （ICCE）. 2010.
[21] Ahonen, Timo, Abdenour Hadid, and Matti Pietikäinen. "Face recognition with local binary patterns." Computer vision-eccv 2004. Springer Berlin Heidelberg, 2004. 469-481.
[22] R. C. Gonzalez and R. E. Woods, Digital Image Processing, 2nd ed., Reading, MA: Addison-Wesley, 1992.
[23] Pizer, Stephen M., et al. "Adaptive histogram equalization and its variations." Computer vision, graphics, and image processing 39.3 （1987）: 355-368.
[24] Zuiderveld, Karel. "Contrast limited adaptive histogram equalization." Graphics gems IV. Academic Press Professional, Inc., 1994.
[25] Turk, M., and Pentland, A. Eigenfaces for recognition. Journal of Cognitive Neuroscience 3 （1991）, 71–86.
[26] Belhumeur, P. N., Hespanha, J., and Kriegman, D. Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 7 （1997）, 711–720.
[27] Hafiane, Adel, Guna Seetharaman, and Bertrand Zavidovique. "Median binary pattern for textures classification." International Conference Image Analysis and Recognition. Springer, Berlin, Heidelberg, 2007.
[28] Jin, Hongliang, et al. "Face detection using improved LBP under Bayesian framework." Image and Graphics （ICIG`04）, Third International Conference on. IEEE, 2004.
[29] Heikkilä, Marko, Matti Pietikäinen, and Cordelia Schmid. "Description of interest regions with center-symmetric local binary patterns." ICVGIP. Vol. 6. 2006.
[30] Ahonen, Timo, and Matti Pietikäinen. "Soft histograms for local binary patterns." Proceedings of the Finnish signal processing symposium, FINSIG. Vol. 5. No. 9. 2007.
[31] Ahonen, Timo, Abdenour Hadid, and Matti Pietikainen. "Face description with local binary patterns: Application to face recognition." IEEE transactions on pattern analysis and machine intelligence 28.12 （2006）: 2037-2041.
[32] Ojala, Timo, Matti Pietikainen, and David Harwood. "Performance evaluation of texture measures with classification based on Kullback discrimination of distributions." Pattern Recognition, 1994. Vol. 1-Conference A: Computer Vision & Image Processing., Proceedings of the 12th IAPR International Conference on. Vol. 1. IEEE, 1994.
描述 碩士
國立政治大學
資訊科學學系
102753013
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0102753013
資料類型 thesis
dc.contributor.advisor 郭耀煌<br>廖文宏zh_TW
dc.contributor.advisor Kuo, Yau-Hwang<br>Liao, Wen-Hungen_US
dc.contributor.author (作者) 林言翰zh_TW
dc.contributor.author (作者) Lin, Yan-Hanen_US
dc.creator (作者) 林言翰zh_TW
dc.creator (作者) Lin, Yan-Hanen_US
dc.date (日期) 2017en_US
dc.date.accessioned 13-九月-2017 14:47:33 (UTC+8)-
dc.date.available 13-九月-2017 14:47:33 (UTC+8)-
dc.date.issued (上傳時間) 13-九月-2017 14:47:33 (UTC+8)-
dc.identifier (其他 識別碼) G0102753013en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/112676-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊科學學系zh_TW
dc.description (描述) 102753013zh_TW
dc.description.abstract (摘要) 近紅外線影像由於其物理特性與成像方式，其紋理細節都有發散模糊不清的現象，對於以紋理為主要特徵的年齡辨識問題而言更具挑戰。本論文主要目的是以近紅外線人臉影像為基礎，找出對近紅外線年齡特徵有最佳描述力的特徵描述子，辨識近紅外線影像中被拍攝者的年齡區間，建構整個年齡層估算機制。
相關研究一部分關注可見光年齡辨識，另一部分則聚焦在近紅外線人臉辨識，目前還沒有近紅外線年齡辨識的相關文獻能參考，如何從接近的研究領域找尋是適當的演算法是本研究遇到的第一個挑戰。在資料庫的部分，FGNET和MORPH常被用於可見光的年齡辨識議題； PolyU和LDHF則用於近紅外線人臉辨識相關研究，在目前沒有近紅外線年齡資料庫的情況，本研究自建RSNIR（Intel RealSense Near-Infrared Age Database），因此如何標準化拍攝環境流程、蒐集穩定的近紅外線影像是本研究面臨的第二個挑戰。
區域性特徵擷取方法的關鍵在於特徵描述子的描述力。本研究以LBP（Local Binary Patterns）為基礎，探討LBP在內的24個特徵描述子，最後實驗測試各個描述子在RSNIR的辨識率，結果發現基本型Fuzzy LBP和擴充型RILBP對近紅外線年齡特徵有最佳描述力。在空間譜子區塊（patch）設計部分，以3x3切割子區塊數的辨識效果最好，反應出其與影像校正時的人臉影像空間定義方式有關。		zh_TW
dc.description.tableofcontents 第一章 緒論 1
1.1　研究背景 1
1.2　研究動機 2
1.3　研究目的 5
1.4　研究架構 5
第二章　人臉影像資料庫 6
2.1　MORPH 6
2.2　FGNET 7
2.3　PolyU 8
2.4　LDHF 10
2.5　RSNIR 11
第三章　特徵擷取（Feature Extraction） 13
3.1　特徵擷取區域 14
3.2　特徵描述子（Feature Descriptor） 15
3.2.1　Local Binary Patterns（LBP） 16
3.2.2　Rotational-Invariance Local Binary Patterns（RILBP） 20
3.2.3　Uniform Local Binary Patterns（ULBP） 22
3.2.4　Rotational-Invariance Uniform Local Binary Patterns（RIULBP） 24
3.2.5　Median Binary Patterns（MBP） 25
3.2.6　Improved Local Binary Patterns（ILBP） 26
3.2.7　Center-Symmetric Local Binary Patterns（CS-LBP） 27
3.2.8　Local Ternary Patterns（LTP） 28
3.2.9　Extended Local Ternary Patterns（ELTP） 29
3.2.10　Center-Symmetric Local Ternary Patterns（CS-LTP） 30
3.2.11　Center-Symmetric Extended Local Ternary Patterns（CS-ELTP） 30
3.2.12　Fuzzy Local Ternary Patterns（Fuzzy LBP） 31
3.2.13　Local Directional Patterns（LDP） 33
第四章　影像前處理 36
4.1　人臉/眼偵測（Face/Eyes Detection） 36
4.2　人臉校正（Face Alignment） 39
4.2.1　邊緣偵側（Edge Detection） 39
4.2.2　內眼角定位 40
4.2.3　人臉校正演算法 41
4.3　影像增強（Image Enhancement） 43
4.3.1　Gamma校正 43
4.3.2　基於直方圖修改的對比度增強 44
第五章　年齡組估測機制 46
5.1　人臉年齡估測理論 46
5.2　人臉年齡評估規範 47
5.3　區域描述子的統計直方圖 49
5.4　分類（Classification） 52
第六章　結果與討論 53
第七章　未來展望 59
附錄一　即時影像擷取 60
1. Intel® RealSense™ 60
2. 3D照相機（3D Camera） 61
3. 軟體開發套件（Software Development Kit，SDK） 62
附錄二　全域/區域特徵擷取實驗結果 63
附錄三　特徵描述子實驗結果 64
3.1　LBP 64
3.2　RILBP 64
3.3　ULBP 65
3.4　RIULBP 65
3.5　MBP 66
3.6　ILBP 66
3.7　CS-LBP 67
3.8　LTP 68
3.9　ELTP 70
3.10　CS-LTP 72
3.11　CS-ELTP 74
3.12　Fuzzy LBP 76
3.13　LDP 78
參考文獻 80		zh_TW
dc.format.extent 8187234 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0102753013en_US
dc.subject (關鍵詞) 年齡估計zh_TW
dc.subject (關鍵詞) 近紅外線影像zh_TW
dc.subject (關鍵詞) 局部二值模式zh_TW
dc.subject (關鍵詞) RealSense攝影機zh_TW
dc.subject (關鍵詞) Age estimationen_US
dc.subject (關鍵詞) Near infrareden_US
dc.subject (關鍵詞) Local Binary Patternsen_US
dc.subject (關鍵詞) RealSense cameraen_US
dc.title (題名) 基於近紅外線影像之年齡層估算機制zh_TW
dc.title (題名) A mechanism for age classification using near-infrared imagesen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) [1] Burnay, S. G., T. L. Williams, and C. H. Jones, eds. Applications of thermal imaging. CRC Press, 1988.
[2] 薛玉利. 基于 Gabor 变换的特征提取及其应用. MS thesis. 山东大学, 2007.
[3] Ojala, Timo, Matti Pietikainen, and Topi Maenpaa. "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns." Pattern Analysis and Machine Intelligence, IEEE Transactions on 24.7 （2002）: 971-987.
[4] Tan, Xiaoyang, and Bill Triggs. "Enhanced local texture feature sets for face recognition under difficult lighting conditions." Analysis and Modeling of Faces and Gestures. Springer Berlin Heidelberg, 2007. 168-182.
[5] Liao, Wen-Hung. "Region description using extended local ternary patterns. "Pattern Recognition （ICPR）, 2010 20th International Conference on. IEEE, 2010.
[6] Liao, Wen-Hung. "Commensurate dimensionality reduction for extended local ternary patterns." Pattern Recognition （ICPR）, 2012 21st International Conference on. IEEE, 2012.
[7] Altman, Naomi S. "An introduction to kernel and nearest-neighbor nonparametric regression." The American Statistician 46.3 （1992）: 175-185.
[8] Guo, Guodong, Stan Z. Li, and Kapluk Chan. "Face recognition by support vector machines." Automatic Face and Gesture Recognition, 2000. Proceedings. Fourth IEEE International Conference on. IEEE, 2000.
[9] Burges, Christopher JC. "A tutorial on support vector machines for pattern recognition." Data mining and knowledge discovery 2.2 （1998）: 121-167.
[10] The FG-NET Aging Database, http://www.fgnet.rsunit.com/, http://www-prima.inrialpes.fr/FGnet/, 2010.
[11] Ricanek Jr, Karl, and Tamirat Tesafaye. "Morph: A longitudinal image database of normal adult age-progression." Automatic Face and Gesture Recognition, 2006. FGR 2006. 7th International Conference on. IEEE, 2006.
[12] MORPH Face Database, http://faceaginggroup.com/, 2010.
[13] PolyU NIR Face Database, http://www4.comp.polyu.edu.hk/~biometrics/polyudb_face.htm, 2010.
[14] Hsu, Rein-Lien, Mohamed Abdel-Mottaleb, and Anil K. Jain. "Face detection in color images." Pattern Analysis and Machine Intelligence, IEEE Transactions on 24.5 （2002）: 696-706.
[15] Yang, Ming-Hsuan, and Narendra Ahuja. "Detecting human faces in color images." Image Processing, 1998. ICIP 98. Proceedings. 1998 International Conference on. Vol. 1. IEEE, 1998.
[16] Viola, Paul, and Michael Jones. "Rapid object detection using a boosted cascade of simple features." Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on. Vol. 1. IEEE, 2001.
[17] Lienhart, Rainer, and Jochen Maydt. "An extended set of haar-like features for rapid object detection." Image Processing. 2002. Proceedings. 2002 International Conference on. Vol. 1. IEEE, 2002.
[18] Liao, Shengcai, et al. "Learning multi-scale block local binary patterns for face recognition." Advances in Biometrics. Springer Berlin Heidelberg, 2007. 828-837.
[19] Fu, Yun, Guodong Guo, and Thomas S. Huang. "Age synthesis and estimation via faces: A survey." Pattern Analysis and Machine Intelligence, IEEE Transactions on 32.11 （2010）: 1955-1976.
[20] Jabid, Taskeed, Md Hasanul Kabir, and Oksam Chae. "Local directional pattern （LDP） for face recognition." 2010 Digest of Technical Papers International Conference on Consumer Electronics （ICCE）. 2010.
[21] Ahonen, Timo, Abdenour Hadid, and Matti Pietikäinen. "Face recognition with local binary patterns." Computer vision-eccv 2004. Springer Berlin Heidelberg, 2004. 469-481.
[22] R. C. Gonzalez and R. E. Woods, Digital Image Processing, 2nd ed., Reading, MA: Addison-Wesley, 1992.
[23] Pizer, Stephen M., et al. "Adaptive histogram equalization and its variations." Computer vision, graphics, and image processing 39.3 （1987）: 355-368.
[24] Zuiderveld, Karel. "Contrast limited adaptive histogram equalization." Graphics gems IV. Academic Press Professional, Inc., 1994.
[25] Turk, M., and Pentland, A. Eigenfaces for recognition. Journal of Cognitive Neuroscience 3 （1991）, 71–86.
[26] Belhumeur, P. N., Hespanha, J., and Kriegman, D. Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 7 （1997）, 711–720.
[27] Hafiane, Adel, Guna Seetharaman, and Bertrand Zavidovique. "Median binary pattern for textures classification." International Conference Image Analysis and Recognition. Springer, Berlin, Heidelberg, 2007.
[28] Jin, Hongliang, et al. "Face detection using improved LBP under Bayesian framework." Image and Graphics （ICIG`04）, Third International Conference on. IEEE, 2004.
[29] Heikkilä, Marko, Matti Pietikäinen, and Cordelia Schmid. "Description of interest regions with center-symmetric local binary patterns." ICVGIP. Vol. 6. 2006.
[30] Ahonen, Timo, and Matti Pietikäinen. "Soft histograms for local binary patterns." Proceedings of the Finnish signal processing symposium, FINSIG. Vol. 5. No. 9. 2007.
[31] Ahonen, Timo, Abdenour Hadid, and Matti Pietikainen. "Face description with local binary patterns: Application to face recognition." IEEE transactions on pattern analysis and machine intelligence 28.12 （2006）: 2037-2041.
[32] Ojala, Timo, Matti Pietikainen, and David Harwood. "Performance evaluation of texture measures with classification based on Kullback discrimination of distributions." Pattern Recognition, 1994. Vol. 1-Conference A: Computer Vision & Image Processing., Proceedings of the 12th IAPR International Conference on. Vol. 1. IEEE, 1994.		zh_TW