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A new face feature point matrix based on geometric features and illumination models for facial attraction analysis
School of Information Science and Technology, Northwest University, Xi'an, China |
In this paper, we propose a 81-point face feature points template that used for face attraction analysis. This template is proposed that based on the AAM model, according to the geometric characteristics and the illumination model. The experimental results demonstrate that, the attraction of human face can be analyzed by the feature vector analysis of human face image quantification and the influence of light intensity on the attraction of human face. By taking the appropriate algorithm, the concept of facial beauty attractiveness can be learned by machine with numeric expressions.
References:
[1] |
A. Asthana, S. Zafeiriou, S. Cheng and M. Pantic, Robust discriminative response map fitting with constrained local models, IEEE Conference on Computer Vision and Pattern Recognition, (2013), 3444-3451. Google Scholar |
[2] |
R. Basri and D. W. Jacobs, Lambertian reflectance and linear subspaces, IEEE Transactions on Pattern Analysis & Machine Intelligence, 25 (2003), 218-233. Google Scholar |
[3] |
F. Chen and D. Zhang, A benchmark for geometric facial beauty study, Lecture Notes in Computer Science, 6165 (2010), 21-32. Google Scholar |
[4] |
Y. Cheon and D. Kim, Natural facial expression recognition using differential-AAM and manifold learning, Pattern Recognition, 42 (2009), 1340-1350. Google Scholar |
[5] |
T. F. Cootes, G. J. Edwards and C. J. Taylor, Active appearance models, European Conference on Computer Vision, (2001), 484-498. Google Scholar |
[6] |
J. H. Langlois and L. A. Roggman, Attractive Faces Are Only Average, Psychological Science, 1 (1990), 115-121. Google Scholar |
[7] |
X.-F. Lu, Chinese research facial pattern types and techniques, China Academy of Fine Arts doctoral dissertation, 2010. Google Scholar |
[8] |
X.-F. Lu, Yuan Dynasty painter Wang Yi's "writing like a secret" and portrait program, Fine Arts., 18 (2005), 72-73. Google Scholar |
[9] |
H.-Y. Mao, Facial beauty attractive characteristics of the analysis and machine learning, South China University of Technology, 2011. Google Scholar |
[10] |
I. Matthews and S. Baker, Active appearance models revisited, International Journal of Computer Vision, 6165 (2004), 135-164. Google Scholar |
[11] |
S. C. Rhee and S. H. Koo, An objective system for measuring facial attractiveness, Plastic & Reconstructive Surgery, 119 (2006), 1953-1954. Google Scholar |
[12] |
A. J. Rubenstein, J. H. Langlois and L. A. Roggman, What makes a face attractive and why: The role of averageness in defining facial beauty, G Rhodes & L 62 Zebrowitz, Facial Attractiveness: Evolutionary, Cognitive, & Social Perspectives, 2002. Google Scholar |
[13] |
Y. Sato, M. D. Wheeler and K. Ikeuchi, Object Shape and Reflectance Modeling from Observation, Modeling from Reality. Springer US, (2001), 95-116. Google Scholar |
[14] |
G. Tzimiropoulos and M. Pantic, Optimization Problems for Fast AAM Fitting in-the-Wild, IEEE International Conference on Computer Vision, (2014), 593-600. Google Scholar |
[15] |
X.-M. Zhang, China United States, Beijing: Xinhua Publishing House, 2005. Google Scholar |
show all references
References:
[1] |
A. Asthana, S. Zafeiriou, S. Cheng and M. Pantic, Robust discriminative response map fitting with constrained local models, IEEE Conference on Computer Vision and Pattern Recognition, (2013), 3444-3451. Google Scholar |
[2] |
R. Basri and D. W. Jacobs, Lambertian reflectance and linear subspaces, IEEE Transactions on Pattern Analysis & Machine Intelligence, 25 (2003), 218-233. Google Scholar |
[3] |
F. Chen and D. Zhang, A benchmark for geometric facial beauty study, Lecture Notes in Computer Science, 6165 (2010), 21-32. Google Scholar |
[4] |
Y. Cheon and D. Kim, Natural facial expression recognition using differential-AAM and manifold learning, Pattern Recognition, 42 (2009), 1340-1350. Google Scholar |
[5] |
T. F. Cootes, G. J. Edwards and C. J. Taylor, Active appearance models, European Conference on Computer Vision, (2001), 484-498. Google Scholar |
[6] |
J. H. Langlois and L. A. Roggman, Attractive Faces Are Only Average, Psychological Science, 1 (1990), 115-121. Google Scholar |
[7] |
X.-F. Lu, Chinese research facial pattern types and techniques, China Academy of Fine Arts doctoral dissertation, 2010. Google Scholar |
[8] |
X.-F. Lu, Yuan Dynasty painter Wang Yi's "writing like a secret" and portrait program, Fine Arts., 18 (2005), 72-73. Google Scholar |
[9] |
H.-Y. Mao, Facial beauty attractive characteristics of the analysis and machine learning, South China University of Technology, 2011. Google Scholar |
[10] |
I. Matthews and S. Baker, Active appearance models revisited, International Journal of Computer Vision, 6165 (2004), 135-164. Google Scholar |
[11] |
S. C. Rhee and S. H. Koo, An objective system for measuring facial attractiveness, Plastic & Reconstructive Surgery, 119 (2006), 1953-1954. Google Scholar |
[12] |
A. J. Rubenstein, J. H. Langlois and L. A. Roggman, What makes a face attractive and why: The role of averageness in defining facial beauty, G Rhodes & L 62 Zebrowitz, Facial Attractiveness: Evolutionary, Cognitive, & Social Perspectives, 2002. Google Scholar |
[13] |
Y. Sato, M. D. Wheeler and K. Ikeuchi, Object Shape and Reflectance Modeling from Observation, Modeling from Reality. Springer US, (2001), 95-116. Google Scholar |
[14] |
G. Tzimiropoulos and M. Pantic, Optimization Problems for Fast AAM Fitting in-the-Wild, IEEE International Conference on Computer Vision, (2014), 593-600. Google Scholar |
[15] |
X.-M. Zhang, China United States, Beijing: Xinhua Publishing House, 2005. Google Scholar |





Feature quantity number | Feature quantity symbol | Feature quantity description |
1 | F1 | Nose and ears width (nose up to the top of the ear) |
4 | F4 | Nose to the height of the forehead center |
5 | F5 | Nose to the eyes of the angle |
6 | F6 | Forehead center to the side of the distance |
7 | F7 | The distance on both sides of the forehead |
Feature quantity number | Feature quantity symbol | Feature quantity description |
1 | F1 | Nose and ears width (nose up to the top of the ear) |
4 | F4 | Nose to the height of the forehead center |
5 | F5 | Nose to the eyes of the angle |
6 | F6 | Forehead center to the side of the distance |
7 | F7 | The distance on both sides of the forehead |
Experimental sample | Slope 1 | Slope 2 | Difference |
1 | 0.0280 | 0.0399 | -0.0119 |
3 | 0.0196 | -0.0402 | 0.0598 |
4 | -00476 | -0.0562 | 0.0086 |
5 | 0.0840 | 0.0224 | 0.0616 |
6 | 0.1369 | 0.1168 | 0.0201 |
Experimental sample | Slope 1 | Slope 2 | Difference |
1 | 0.0280 | 0.0399 | -0.0119 |
3 | 0.0196 | -0.0402 | 0.0598 |
4 | -00476 | -0.0562 | 0.0086 |
5 | 0.0840 | 0.0224 | 0.0616 |
6 | 0.1369 | 0.1168 | 0.0201 |
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