计算人脸医学美学研究
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摘要
人类对自身之美的研究已有几千年的历史了,人类对美的不断追求,促进了人的审美意识不断提高,同时也促进了社会的发展,而面部美学又是其中的研究重点。国内外很多学者对于美及其规律也得到出了一些初步结论,但是传统的人脸美学研究由于其主观性、手动化等缺点严重阻碍了其理论发展和实际应用。本文基于传统的医学美学与理论美学提出了计算人脸医学美学概念,将面部美学研究量化与自动化,并希望其在临床美容,整形与修复当中具有指导意义。
首先,本文基于传统的人体测量学和解剖学对人脸几何形状进行了标准化定义,即对描述人脸几何特征的特征点给出标准化定义,并通过稳定性分析对其数量选择给出了一系列准则,从而将底层误差限制在最小范围内。
其次,本文使用主动轮廓模型和改进后的主动形状模型对面部特征进行提取,前者虽然基于可变模板,但是以牺牲模板的鲁棒性来换取可变性;后者训练时间过长,而且搜索过程中易受目标结构周围物体影响。因此本文使用改进后的主动形状模型进行面部特征点的提取,将提取出的特征点构造成一个更具有视觉感知意义的面部美学向量。
然后,使用支持向量回归技术建立面部美学向量与人脸美丽指数之间的回归模型,对于新的图像使用该模型求与其面部美学向量相接近但具有更高美丽指数的目标向量。
最后,使用移动最小二乘图像变换技术,并以原始面部美学向量和其目标向量为控制条件,对面部特征进行变换,从而达到美容,整形和修复等目的。
Research on human's esthetics has a history of several thousand years, in which facial esthetics is its essential focus. But traditional facial esthetics research has some inherent shortcomings, such as subjectivity during the research process, time consuming and so on, all of this prevent the theoretical progress and practical application of this field. So in this paper, based on Medical esthetics and traditional theoretical we propose the concept of Computed Facial Medical Esthetics, aiming at quantifying and automating this research, we also hope that the result can be used to guide clinical application, such as cosmetology, plastics surgery and so on.
First, we give a comprehensive definition for feature points which are used to depict facial features.As for the number of these points we use an optimal selection algorithm by analyzing their stability. So we can reduce the low-level inaccuracy.
Secondly, we aim at extracting these feature points automatically, there are some algorithms based on flexible models, of which the active contour models and active shape models has the best performance. We analyze them and make some improvements, using these algorithms we can enlarge our research on large amount of samples.
Thirdly, after obtaining these feature points, we do not use them directly; instead we construct a geometrical facial beauty vector of visual sensation significance, which consists of outline information and the facial parameter information. Then we obtain a model between this vector and beauty indices by support vector regression technology.
Then for a given facial image, we can extract its feature points, construct GFBV and use SVR and optimization to get a target vector, which is near to this GFBV but has higher beauty index andt map to facial feature points.
Finally, using these target feature points and original feature points we can implement image deformation, which can produce a target face with higher beauty index. The result can be used to guide cosmetology, plastics surgery and so on.
首先,本文基于传统的人体测量学和解剖学对人脸几何形状进行了标准化定义,即对描述人脸几何特征的特征点给出标准化定义,并通过稳定性分析对其数量选择给出了一系列准则,从而将底层误差限制在最小范围内。
其次,本文使用主动轮廓模型和改进后的主动形状模型对面部特征进行提取,前者虽然基于可变模板,但是以牺牲模板的鲁棒性来换取可变性;后者训练时间过长,而且搜索过程中易受目标结构周围物体影响。因此本文使用改进后的主动形状模型进行面部特征点的提取,将提取出的特征点构造成一个更具有视觉感知意义的面部美学向量。
然后,使用支持向量回归技术建立面部美学向量与人脸美丽指数之间的回归模型,对于新的图像使用该模型求与其面部美学向量相接近但具有更高美丽指数的目标向量。
最后,使用移动最小二乘图像变换技术,并以原始面部美学向量和其目标向量为控制条件,对面部特征进行变换,从而达到美容,整形和修复等目的。
Research on human's esthetics has a history of several thousand years, in which facial esthetics is its essential focus. But traditional facial esthetics research has some inherent shortcomings, such as subjectivity during the research process, time consuming and so on, all of this prevent the theoretical progress and practical application of this field. So in this paper, based on Medical esthetics and traditional theoretical we propose the concept of Computed Facial Medical Esthetics, aiming at quantifying and automating this research, we also hope that the result can be used to guide clinical application, such as cosmetology, plastics surgery and so on.
First, we give a comprehensive definition for feature points which are used to depict facial features.As for the number of these points we use an optimal selection algorithm by analyzing their stability. So we can reduce the low-level inaccuracy.
Secondly, we aim at extracting these feature points automatically, there are some algorithms based on flexible models, of which the active contour models and active shape models has the best performance. We analyze them and make some improvements, using these algorithms we can enlarge our research on large amount of samples.
Thirdly, after obtaining these feature points, we do not use them directly; instead we construct a geometrical facial beauty vector of visual sensation significance, which consists of outline information and the facial parameter information. Then we obtain a model between this vector and beauty indices by support vector regression technology.
Then for a given facial image, we can extract its feature points, construct GFBV and use SVR and optimization to get a target vector, which is near to this GFBV but has higher beauty index andt map to facial feature points.
Finally, using these target feature points and original feature points we can implement image deformation, which can produce a target face with higher beauty index. The result can be used to guide cosmetology, plastics surgery and so on.
引文
1王大为,刘旺,钱国正.华东地区汉族青年人面部垂直测量的分析与比较.中华整形外科杂志. 2003, 19(1):75~76
2闻可,黄金龙.江苏地区貌美人群面部比例与古典美学标准之比较.现代医学. 2005, 33(3):182~184
3熊耀阳,孙健,张富强.上海地区成人鼻面部的观察和测量. CHINESE JOURNAL OF ANATOMY. 2006, 29(6):784~786
4赵碧蓉,李立,白玉兴,杨民,裴海燕.四川籍美貌青年颜面比例的美学分析.华西口腔医学杂志. 1997, 15(2):135~137
5 Alley, T. R., & Cunningham, M. R.Averaged faces are attractive, but very attractive faces are not average. Psychological Science.1991, 2(2):123~125
6高燕.黄金分割定律在重睑美容术中的应用.南通医学院学报.1995, 15(3):457~458
7 E. Holland, Marquardt’s Phi Mask. Pitfalls of Relying on Fashion Models and the Golden Ratio to Describe a Beautiful Face. Aesth Plast Surg. 2008, 32(2):200~208
8 Wang Dawei, Qian Guozheng, Zhang Mingli, Leslie G. Farkas. Differences in Horizontal, Neoclassical Facial Canons in Chinese (Han) and North American Caucasian Populations. Aesth. Plast. Surg. 1997, 21(4):265~269
9 Marek Dobke, Christopher Chung, Kazuaki Takabe, San Diego. Facial Aesthetic Preferences Among Asian Women: Are All Oriental Asians the Same? Aesth. Plast. Surg. 2006, 30(3):342~347
10 N. Patel. Numeric Expression of Aesthetics and Beauty. Aesth Plast Surg. 2008, 32(2):217~219
11 Alaa El-Hussuna. Statistical Variation of Three Dimensional Face Models. IT-University of Copenhagen Multimedia Technologies Master Thesis project. 2003
12 Raymond Edler, Pragati Agarwal, David Wertheim, and Darrel Greenhill. The use of anthropometric proportion indices in the measurement of facial attractiveness. European Journal of Orthodontics. 2006, 28(3):274–281
13 M. Kass, A. Witkin, and D. Terzopoulos. Snakes: Active contour models. International Journal of Computer Vision. 1987, 1(4):321-331
14 Chenyang Xu and Jerry L. Prince. Snakes, Shape, and Gradient Vector Flow. IEEE Transactions on Image Processing. 1998, 7(3):359~369
15 C. Xu and J.L. Prince. Gradient Vector Flow: A New External Force for Snakes.Proc. IEEE Conf. on Comp. Vis. Patt. Recog. 1997:66~71
16 T.F.Cootes, C.J.Taylor. Active Shape Models - Smart Snakes. British Machine Vision Conference. 1992:266~275
17 T.F. Cootes, D. Cooper, C.J. Taylor and J. Graham. Active Shape Models - Their Training and Application. Computer Vision and Image Understanding. 1995, 61(1): 38~59
18 T.F.Cootes, A.Hill, C.J.Taylor, J.Haslam. The Use of Active Shape Models for Locating Structures in Medical Images. Image and Vision Computing. 1994, 12(6):355~366
19 T. F. Cootes, C. J.Taylor, D. Cooper, and J. Graham. Training Models of Shape from Sets of Examples. 3rd British Machine Vision Conference. 1992:9~18
20 G. Edwards, C. J. Taylor, and T. F. Cootes. Interpreting Face Images Using Active Ap-pearance Models. 3rd International Conference on Automatic Face and Gesture Recog-nition. 1998:300~305
21 B. van Ginneken, A.F.Frangi, J.J.Stall, and B. ter Haar Romeny. Active Shape Model Segmentation with Optimal Features. IEEE-TMI. 2002,21(8):924~933
22 T. F. Cootes and C. J. Taylor. Statistical Models of Appearance for Computer Vision. Technical Report: The University of Manchester School of Medicine. 2004
23 Lindsay I Smith. A tutorial on Principal Components Analysis. 2002
24 T.F.Cootes, C.J.Taylor, A.Lanitis, D.H.Cooper and J.Graham. Building and Using Flexible Models Incorporating Grey-Level Information. Proc. Fourth International Conference on Computer Vision. 1993:242-246
25 T.F.Cootes, C.J.Taylor, Active Shape Model Search using Local Grey-Level Models: A Quantitative Evaluation. Proc. British Machine Vision Conference. 1993:639-648
26 Henry A. Rowley, Shumeet Baluja, and Takeo Kanade. Neural Network-Based Face Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1998, 20(1):22~38
27 P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. Computer Vision and Pattern Recognition Conference. 2001, 1:511~518
28 T.F.Cootes, C.J.Taylor, A.Lanitis. Multi-Resolution Search with Active Shape Models, Proc. International Conference on Pattern Recognition. 1994, 1:610-612
29 E. H. Adelson, C. H. Anderson, J. R. Bergen, P. J. Burt, and J. M. Ogden. Pyramid Methods in Image Processing. RCA Engineer. 1984, 29(6):33~41
30 Bishop C M. Neural networks for pattern recognition. Oxford University Press. 1996
31 Vapnik V N, Chervonenkis AJa. On the uniform convergence of relativefrequencies of events to their probabilities. Doklady Akademii Nauk USSR. 1968
32 Blumer A, Ehrenfeucht A, Haussler D et al. Learnability and the Vapnik-Chervonenkis dimension. Journal of the ACM. 1989, 36(4): 929~965
33 Vapnik V N, Chervonenkis AJa. Necessary and sufficient conditions for the uniform convergence of the means to their expectation. The Indian Journal of Statistics. 1985, 47(2): 203~208
34 Cortes C, Vapnik V N. Support vector networks. Machine Learning. 1995, 20(3): 273~297
35周伟达,张莉焦,李成.一种改进的推广能力衡量准则.计算机学报. 2003, 26(5):598~604
36 V. Vapnik and A. Chervonenkis. Theory of Pattern Recognition. Nauka, Moscow. 1974
37 John S T, Peter L, Bartlett R C et al. A framework for structural risk minimization. In Proc. 9th Annu. Conf. on Comput. Learning Theory. 1996: 68~76
38 B. Sch?lkopf. Support Vector Learning. R. Oldenbourg Verlag Publication. 1997
39 Alex J. Smola, Bernhard Sch?lkopf. A Tutorial on Support Vector Regression. Royal Holloway College, NeuroCOLT Technical Report. 1998
40魏宏业,吕永波,何威,张仲义,基于数据挖掘的企业合作伙伴的寻求方法研究仁[J].管理科学学报. 2004, 7(1):60~65
41张浩,赵相东.数据挖掘在石化企业中的应用.计算机工程与应用. 2004, 40(30): 208~210
42张辉,张浩,徐征,陆剑峰.基于支持向量机的供应链伙伴企业选择方法的研究计算机集成制造系统. 2004, 10(7):796~71
43张辉,张浩,陆剑峰,严隽薇.支持向量机在流程型企业决策支持系统中的应用.计算机工程与应用. 2004, 40(23):209~211
44陆剑雪,侍洪波.基于支持向量机的化工过程故障诊断.华东理工大学学报. 2004, 30(3):315~31
45王李东,李志宇,文劲宇.基于SVR算法的短期快速预测研究.继电器. 2005, 33(9) :17~21
46 Ping FengPai, Chih Sheng Lin. A hybrid ARIMA and Support Vector Machines Model in Stock Price Forecasting. The International Journal of Management Science. 2004, 33(6):497~505
47 Ping Feng Pai, Wei Chiang Hong. Support Vector Machines with Simulated Annealing Algorithms in Electricity Load Forecast. Energy Conversation & Management. 2005, 46(7):2669~2688
48 C. Cortes, V. Vapnik. Support vector networks. Machine Learning. 1995, 20(3):273–297
49 R. Fletcher. Practical Methods of Optimization. John Wiley and Sons. 1989
50 W. Karush. Minima of functions of several variables with inequalities as side constraints. Master’s thesis, Dept. of Mathematics, Univ. of Chicago.1939
51 M. A. Aizerman, E. M. Braverman et.al. Theoretical foundations of the potential function method in pattern recognition learning. Automation and Remote Control. 1964, 25:821–837
52 J. Mercer. Functions of positive and negative type and their connection with the theory of integral equations. Philosophical Transactions of the Royal Society of London.1909, 209:415~446
53 DouglasB. Smythe. A Two-Pass Mesh Warping Algorithm for Object Transformation and Image Interpolaiton. Technical Memo 1030, Computer Graphics Department, Lucasfilm Ltd. 1990
54 WARREN, J., JU, T., EICHELE, G., THALLER, C., CHIU, W., CARSON, J. A geometric database for gene expression data. Eurographics Symposium on Geometry Processing. 2003, 43:166~176
55 SEDERBERG, T. W., AND PARRY, S. R. Free-form deformation of solid geometric models. In Proceedings of ACM SIGGRAPH. 1986, 20(4):151~160
56 LEE, S.-Y., CHWA, K.-Y., SHIN, S. Y. Image metamorphosis using snakes and free-form deformations. Proceedings of the 22nd annual conference on Computer graphics and interactive techniques.1995:439~448
57 LEVIN, D. The approximation power of moving leastsquares. Mathematics of Computation. 1998, 67(224):1517~1531
58 ALEXA, M., COHEN-OR, D., AND LEVIN, D. As rigid-as-possible shape interpolation. In Proceedings of ACM SIGGRAPH 2000. 2000:157~164
59 HORN, B. Closed-form solution of absolute orientation using unit quaternions. Journal of the Optical Society of America.1987, 4(4):629~642
60 WOLBERG, G. Image morphing: a survey. The Visual Computer. 1998, 14(8/9):360~372
61马德水.人脸美学的统计特性以及自动化评价的研究.哈尔滨工业大学硕士论文. 2007
2闻可,黄金龙.江苏地区貌美人群面部比例与古典美学标准之比较.现代医学. 2005, 33(3):182~184
3熊耀阳,孙健,张富强.上海地区成人鼻面部的观察和测量. CHINESE JOURNAL OF ANATOMY. 2006, 29(6):784~786
4赵碧蓉,李立,白玉兴,杨民,裴海燕.四川籍美貌青年颜面比例的美学分析.华西口腔医学杂志. 1997, 15(2):135~137
5 Alley, T. R., & Cunningham, M. R.Averaged faces are attractive, but very attractive faces are not average. Psychological Science.1991, 2(2):123~125
6高燕.黄金分割定律在重睑美容术中的应用.南通医学院学报.1995, 15(3):457~458
7 E. Holland, Marquardt’s Phi Mask. Pitfalls of Relying on Fashion Models and the Golden Ratio to Describe a Beautiful Face. Aesth Plast Surg. 2008, 32(2):200~208
8 Wang Dawei, Qian Guozheng, Zhang Mingli, Leslie G. Farkas. Differences in Horizontal, Neoclassical Facial Canons in Chinese (Han) and North American Caucasian Populations. Aesth. Plast. Surg. 1997, 21(4):265~269
9 Marek Dobke, Christopher Chung, Kazuaki Takabe, San Diego. Facial Aesthetic Preferences Among Asian Women: Are All Oriental Asians the Same? Aesth. Plast. Surg. 2006, 30(3):342~347
10 N. Patel. Numeric Expression of Aesthetics and Beauty. Aesth Plast Surg. 2008, 32(2):217~219
11 Alaa El-Hussuna. Statistical Variation of Three Dimensional Face Models. IT-University of Copenhagen Multimedia Technologies Master Thesis project. 2003
12 Raymond Edler, Pragati Agarwal, David Wertheim, and Darrel Greenhill. The use of anthropometric proportion indices in the measurement of facial attractiveness. European Journal of Orthodontics. 2006, 28(3):274–281
13 M. Kass, A. Witkin, and D. Terzopoulos. Snakes: Active contour models. International Journal of Computer Vision. 1987, 1(4):321-331
14 Chenyang Xu and Jerry L. Prince. Snakes, Shape, and Gradient Vector Flow. IEEE Transactions on Image Processing. 1998, 7(3):359~369
15 C. Xu and J.L. Prince. Gradient Vector Flow: A New External Force for Snakes.Proc. IEEE Conf. on Comp. Vis. Patt. Recog. 1997:66~71
16 T.F.Cootes, C.J.Taylor. Active Shape Models - Smart Snakes. British Machine Vision Conference. 1992:266~275
17 T.F. Cootes, D. Cooper, C.J. Taylor and J. Graham. Active Shape Models - Their Training and Application. Computer Vision and Image Understanding. 1995, 61(1): 38~59
18 T.F.Cootes, A.Hill, C.J.Taylor, J.Haslam. The Use of Active Shape Models for Locating Structures in Medical Images. Image and Vision Computing. 1994, 12(6):355~366
19 T. F. Cootes, C. J.Taylor, D. Cooper, and J. Graham. Training Models of Shape from Sets of Examples. 3rd British Machine Vision Conference. 1992:9~18
20 G. Edwards, C. J. Taylor, and T. F. Cootes. Interpreting Face Images Using Active Ap-pearance Models. 3rd International Conference on Automatic Face and Gesture Recog-nition. 1998:300~305
21 B. van Ginneken, A.F.Frangi, J.J.Stall, and B. ter Haar Romeny. Active Shape Model Segmentation with Optimal Features. IEEE-TMI. 2002,21(8):924~933
22 T. F. Cootes and C. J. Taylor. Statistical Models of Appearance for Computer Vision. Technical Report: The University of Manchester School of Medicine. 2004
23 Lindsay I Smith. A tutorial on Principal Components Analysis. 2002
24 T.F.Cootes, C.J.Taylor, A.Lanitis, D.H.Cooper and J.Graham. Building and Using Flexible Models Incorporating Grey-Level Information. Proc. Fourth International Conference on Computer Vision. 1993:242-246
25 T.F.Cootes, C.J.Taylor, Active Shape Model Search using Local Grey-Level Models: A Quantitative Evaluation. Proc. British Machine Vision Conference. 1993:639-648
26 Henry A. Rowley, Shumeet Baluja, and Takeo Kanade. Neural Network-Based Face Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1998, 20(1):22~38
27 P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. Computer Vision and Pattern Recognition Conference. 2001, 1:511~518
28 T.F.Cootes, C.J.Taylor, A.Lanitis. Multi-Resolution Search with Active Shape Models, Proc. International Conference on Pattern Recognition. 1994, 1:610-612
29 E. H. Adelson, C. H. Anderson, J. R. Bergen, P. J. Burt, and J. M. Ogden. Pyramid Methods in Image Processing. RCA Engineer. 1984, 29(6):33~41
30 Bishop C M. Neural networks for pattern recognition. Oxford University Press. 1996
31 Vapnik V N, Chervonenkis AJa. On the uniform convergence of relativefrequencies of events to their probabilities. Doklady Akademii Nauk USSR. 1968
32 Blumer A, Ehrenfeucht A, Haussler D et al. Learnability and the Vapnik-Chervonenkis dimension. Journal of the ACM. 1989, 36(4): 929~965
33 Vapnik V N, Chervonenkis AJa. Necessary and sufficient conditions for the uniform convergence of the means to their expectation. The Indian Journal of Statistics. 1985, 47(2): 203~208
34 Cortes C, Vapnik V N. Support vector networks. Machine Learning. 1995, 20(3): 273~297
35周伟达,张莉焦,李成.一种改进的推广能力衡量准则.计算机学报. 2003, 26(5):598~604
36 V. Vapnik and A. Chervonenkis. Theory of Pattern Recognition. Nauka, Moscow. 1974
37 John S T, Peter L, Bartlett R C et al. A framework for structural risk minimization. In Proc. 9th Annu. Conf. on Comput. Learning Theory. 1996: 68~76
38 B. Sch?lkopf. Support Vector Learning. R. Oldenbourg Verlag Publication. 1997
39 Alex J. Smola, Bernhard Sch?lkopf. A Tutorial on Support Vector Regression. Royal Holloway College, NeuroCOLT Technical Report. 1998
40魏宏业,吕永波,何威,张仲义,基于数据挖掘的企业合作伙伴的寻求方法研究仁[J].管理科学学报. 2004, 7(1):60~65
41张浩,赵相东.数据挖掘在石化企业中的应用.计算机工程与应用. 2004, 40(30): 208~210
42张辉,张浩,徐征,陆剑峰.基于支持向量机的供应链伙伴企业选择方法的研究计算机集成制造系统. 2004, 10(7):796~71
43张辉,张浩,陆剑峰,严隽薇.支持向量机在流程型企业决策支持系统中的应用.计算机工程与应用. 2004, 40(23):209~211
44陆剑雪,侍洪波.基于支持向量机的化工过程故障诊断.华东理工大学学报. 2004, 30(3):315~31
45王李东,李志宇,文劲宇.基于SVR算法的短期快速预测研究.继电器. 2005, 33(9) :17~21
46 Ping FengPai, Chih Sheng Lin. A hybrid ARIMA and Support Vector Machines Model in Stock Price Forecasting. The International Journal of Management Science. 2004, 33(6):497~505
47 Ping Feng Pai, Wei Chiang Hong. Support Vector Machines with Simulated Annealing Algorithms in Electricity Load Forecast. Energy Conversation & Management. 2005, 46(7):2669~2688
48 C. Cortes, V. Vapnik. Support vector networks. Machine Learning. 1995, 20(3):273–297
49 R. Fletcher. Practical Methods of Optimization. John Wiley and Sons. 1989
50 W. Karush. Minima of functions of several variables with inequalities as side constraints. Master’s thesis, Dept. of Mathematics, Univ. of Chicago.1939
51 M. A. Aizerman, E. M. Braverman et.al. Theoretical foundations of the potential function method in pattern recognition learning. Automation and Remote Control. 1964, 25:821–837
52 J. Mercer. Functions of positive and negative type and their connection with the theory of integral equations. Philosophical Transactions of the Royal Society of London.1909, 209:415~446
53 DouglasB. Smythe. A Two-Pass Mesh Warping Algorithm for Object Transformation and Image Interpolaiton. Technical Memo 1030, Computer Graphics Department, Lucasfilm Ltd. 1990
54 WARREN, J., JU, T., EICHELE, G., THALLER, C., CHIU, W., CARSON, J. A geometric database for gene expression data. Eurographics Symposium on Geometry Processing. 2003, 43:166~176
55 SEDERBERG, T. W., AND PARRY, S. R. Free-form deformation of solid geometric models. In Proceedings of ACM SIGGRAPH. 1986, 20(4):151~160
56 LEE, S.-Y., CHWA, K.-Y., SHIN, S. Y. Image metamorphosis using snakes and free-form deformations. Proceedings of the 22nd annual conference on Computer graphics and interactive techniques.1995:439~448
57 LEVIN, D. The approximation power of moving leastsquares. Mathematics of Computation. 1998, 67(224):1517~1531
58 ALEXA, M., COHEN-OR, D., AND LEVIN, D. As rigid-as-possible shape interpolation. In Proceedings of ACM SIGGRAPH 2000. 2000:157~164
59 HORN, B. Closed-form solution of absolute orientation using unit quaternions. Journal of the Optical Society of America.1987, 4(4):629~642
60 WOLBERG, G. Image morphing: a survey. The Visual Computer. 1998, 14(8/9):360~372
61马德水.人脸美学的统计特性以及自动化评价的研究.哈尔滨工业大学硕士论文. 2007