Figure 1.
68 feature points detected face map
-
Previous Article
A dynamical system study for the ecological development of mineral resources in minority areas
- DCDS-S Home
- This Issue
-
Next Article
Major project risk assessment method based on BP neural network
August & September
2019, 12(4&5): 1065-1072.
doi: 10.3934/dcdss.2019073
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.
Mathematics Subject Classification:
68T01.
Citation:
Jian Zhao, Fang Deng, Jian Jia, Chunmeng Wu, Haibo Li, Yuan Shi, Shunli Zhang. A new face feature point matrix based on geometric features and illumination models for facial attraction analysis. Discrete & Continuous Dynamical Systems - S,
2019, 12
(4&5)
: 1065-1072.
doi: 10.3934/dcdss.2019073
![]()
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 |
Figure 2.
7-dimensional geometric features
Figure 3.
The feature points of the geometric features in this paper
Figure 4.
The feature point diagram of the illumination model
Figure 5.
Improved light intensity map
Table 1.
7-dimensional distance feature vector description.
| 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 |
Table 2.
The slope of the nose of the ears
| 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 |
| [1] |
A. K. Misra, Anupama Sharma, Jia Li. A mathematical model for control of vector borne diseases through media campaigns. Discrete & Continuous Dynamical Systems - B, 2013, 18 (7) : 1909-1927. doi: 10.3934/dcdsb.2013.18.1909 |
| [2] |
Yves Dumont, Frederic Chiroleu. Vector control for the Chikungunya disease. Mathematical Biosciences & Engineering, 2010, 7 (2) : 313-345. doi: 10.3934/mbe.2010.7.313 |
| [3] |
M. R. S. Kulenović, J. Marcotte, O. Merino. Properties of basins of attraction for planar discrete cooperative maps. Discrete & Continuous Dynamical Systems - B, 2021, 26 (5) : 2721-2737. doi: 10.3934/dcdsb.2020202 |
| [4] |
Fritz Gesztesy, Helge Holden, Johanna Michor, Gerald Teschl. The algebro-geometric initial value problem for the Ablowitz-Ladik hierarchy. Discrete & Continuous Dynamical Systems - A, 2010, 26 (1) : 151-196. doi: 10.3934/dcds.2010.26.151 |
| [5] |
Lei Liu, Li Wu. Multiplicity of closed characteristics on $ P $-symmetric compact convex hypersurfaces in $ \mathbb{R}^{2n} $. Discrete & Continuous Dynamical Systems - A, 2020 doi: 10.3934/dcds.2020378 |
| [6] |
Jan Prüss, Laurent Pujo-Menjouet, G.F. Webb, Rico Zacher. Analysis of a model for the dynamics of prions. Discrete & Continuous Dynamical Systems - B, 2006, 6 (1) : 225-235. doi: 10.3934/dcdsb.2006.6.225 |
| [7] |
Johannes Kellendonk, Lorenzo Sadun. Conjugacies of model sets. Discrete & Continuous Dynamical Systems - A, 2017, 37 (7) : 3805-3830. doi: 10.3934/dcds.2017161 |
| [8] |
Didier Bresch, Thierry Colin, Emmanuel Grenier, Benjamin Ribba, Olivier Saut. A viscoelastic model for avascular tumor growth. Conference Publications, 2009, 2009 (Special) : 101-108. doi: 10.3934/proc.2009.2009.101 |
| [9] |
Ondrej Budáč, Michael Herrmann, Barbara Niethammer, Andrej Spielmann. On a model for mass aggregation with maximal size. Kinetic & Related Models, 2011, 4 (2) : 427-439. doi: 10.3934/krm.2011.4.427 |
| [10] |
Martin Bohner, Sabrina Streipert. Optimal harvesting policy for the Beverton--Holt model. Mathematical Biosciences & Engineering, 2016, 13 (4) : 673-695. doi: 10.3934/mbe.2016014 |
| [11] |
Juan Manuel Pastor, Javier García-Algarra, Javier Galeano, José María Iriondo, José J. Ramasco. A simple and bounded model of population dynamics for mutualistic networks. Networks & Heterogeneous Media, 2015, 10 (1) : 53-70. doi: 10.3934/nhm.2015.10.53 |
| [12] |
Chin-Chin Wu. Existence of traveling wavefront for discrete bistable competition model. Discrete & Continuous Dynamical Systems - B, 2011, 16 (3) : 973-984. doi: 10.3934/dcdsb.2011.16.973 |
| [13] |
Michael Grinfeld, Amy Novick-Cohen. Some remarks on stability for a phase field model with memory. Discrete & Continuous Dynamical Systems - A, 2006, 15 (4) : 1089-1117. doi: 10.3934/dcds.2006.15.1089 |
| [14] |
Alba Málaga Sabogal, Serge Troubetzkoy. Minimality of the Ehrenfest wind-tree model. Journal of Modern Dynamics, 2016, 10: 209-228. doi: 10.3934/jmd.2016.10.209 |
| [15] |
Raghda A. M. Attia, Dumitru Baleanu, Dianchen Lu, Mostafa M. A. Khater, El-Sayed Ahmed. Computational and numerical simulations for the deoxyribonucleic acid (DNA) model. Discrete & Continuous Dynamical Systems - S, 2021 doi: 10.3934/dcdss.2021018 |
| [16] |
Seung-Yeal Ha, Jinwook Jung, Jeongho Kim, Jinyeong Park, Xiongtao Zhang. A mean-field limit of the particle swarmalator model. Kinetic & Related Models, , () : -. doi: 10.3934/krm.2021011 |
| [17] |
Paula A. González-Parra, Sunmi Lee, Leticia Velázquez, Carlos Castillo-Chavez. A note on the use of optimal control on a discrete time model of influenza dynamics. Mathematical Biosciences & Engineering, 2011, 8 (1) : 183-197. doi: 10.3934/mbe.2011.8.183 |
| [18] |
Martial Agueh, Reinhard Illner, Ashlin Richardson. Analysis and simulations of a refined flocking and swarming model of Cucker-Smale type. Kinetic & Related Models, 2011, 4 (1) : 1-16. doi: 10.3934/krm.2011.4.1 |
| [19] |
Ronald E. Mickens. Positivity preserving discrete model for the coupled ODE's modeling glycolysis. Conference Publications, 2003, 2003 (Special) : 623-629. doi: 10.3934/proc.2003.2003.623 |
| [20] |
Rui Hu, Yuan Yuan. Stability, bifurcation analysis in a neural network model with delay and diffusion. Conference Publications, 2009, 2009 (Special) : 367-376. doi: 10.3934/proc.2009.2009.367 |
2019 Impact Factor: 1.233
Tools
Metrics
Other articles
by authors
[Back to Top]