Female Facial Beauty
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FEMALE F ACIAL BEAUTY ANALYSIS FOR ASSESSMENT OF F ACIAL A TTRACTIVENESS Dr. Qaim Mehdi Rizvi Dept. of Computer Science Qassim University, Kingdom of Saudi Arabia
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It presents a hybrid approach to estimate female facial beauty based on Machine Learning techniques. We use a combination of two approaches: Beauty Mask and Facial Proportions, to find the features that constitute Ideal Female facial beauty and thus, develop a female facial beauty scoring system based on the same. The dataset used in this work consists of 30 images being rated by 29 people. These are the front facial images of Winners, 1st Runner-up and 2nd Runner-up of Miss Universe Beauty Pageant from 2002 to 2011. Images are represented by a 50 element vector consisting of control points being selected manually with reference to the Beauty Mask. These points are used to calculate a total of 12 distances and 7 ratios for each image. These distances and ratios are also calculated for the Beauty Mask, and the final score is given on the basis of similarity between the respective ratios. A correlation of 67.78% shows the validity of our approach.
Transcript of Female Facial Beauty
FEMALE FACIAL BEAUTY ANALYSIS FOR ASSESSMENT OF FACIAL ATTRACTIVENESS
Dr. Qaim Mehdi Rizvi Dept. of Computer Science Qassim University, Kingdom of Saudi Arabia
Outline of Research Work Research Objectives
Why study Attractiveness?
Facial Beauty Hypothesis
Research Methodology
Test Results
Future Scope
Research Objectives Beauty Scoring System
To develop a reliable facial beauty scoring system which scores faces based on their facial attractiveness.
Suggestions to improve facial beauty To provide some cosmetic and facial surgery suggestions, which will help the user to improve her facial beauty.
Auto-beautification To output an auto-beautified facial image of the input image and tell up to what extent the face can be beautified.
Why Study Attractiveness Attractiveness Judgments influence key Social outcomes across the
lifespan!
People preferentially mate, date, associate with, employ, and even vote for physically attractive individuals.
Worldwide Annual expenditure on cosmetics: $18 Billion while Annual expenditures required to eliminate hunger and malnutrition: $19 Billion.
Facial Beauty Hypothesis Symmetry
Golden Ratio & The Beauty Mask
Averageness
Symmetry Hypothesis Symmetric Faces tend to be more attractive than the asymmetric
faces.
Symmetric left-left and right-right counter parts of asymmetric faces also tend to be more attractive than original face.
Golden Ratio & The Beauty Mask Dr. Stephen Marquardt has developed a Beauty Mask for Ideal
Beautiful Female Face.
Warping of a face to this beauty mask makes it more beautiful.
Less Attractive Face
More Attractive Face
Beauty Mask
+ =
Theory of Averageness
+ + =
Research Methodology
Training Module
• Dataset Collection
• Rating Collection
Application Module
• Algorithm Design • Application Design
Training Dataset
Rating Method
Based on the Pageant’s Judges’ Scores
Winner 5
1st runner-Up 4
2nd Runner-Up 3
Internal Rating (out of 5)
Based on Ratings of our experts collected through survey (1,2,3,4 or 5).
External Rating (out of 5)
Quantification of Facial Beauty Facial Geometry
After an exhaustive correlation analysis performed on more than 60 facial distances
12 Distances are selected.
7 ratios are calculated.
Beauty Mask Same distances and ratios are calculated for the MBA’s Beauty Mask.
Correlation between the ratios and ratings
Scores Database
Training Algorithm
Image Database Beauty Mask Distance & Ratio
Calculator
Difference Calculator
Correlation Analysis & Ratio Filter
Filtered Ratios
Algorithm Implementation
Correlation Analysis & Ratio Filter
Suggestion Generator
Difference Calculator
Distance & Ratio Calculator
Scores
6.7
8.6 9.1
Min. Difference
Max. Difference
Difference
RI
RM
Why Ratios & not exact Differences To define a Universal Rule
If exact differences were used It’ll be limited to a particular race.
Using ratios
Makes it suitable for generalization over variety of faces.
Application Design Data Collection Application
A Java Applet with MS Access 2010 database connectivity.
Main Application A MATLAB program analysis and implementation.
Test Results SL SCORE NO OF CELEBRITIES (%)
1 8 – 10 24 (80 %)
2 6 – 8 6 (20 %)
3 4 – 6 0 (0 %)
4 2 – 4 0 (0 %)
5 0 – 2 0 (0 %)
Celebrities’ Faces Tested for 30 faces of celebrities and
models.
Agreement with Averageness Hypothesis Average female face (of 64 faces) scored 9.22/10.
Test Results Agreement with previous research results.
Beautiful
(9.49)
Good Looking
(8.02) Common Looking
(8.91) Poor Looking
(4.51)
Asian Beauty
(9.17)
Caucasian Beauty
(8.98) Ugly
(1.74)
Unattractive
(3.27)
Our test results were very motivated and we
got 87.5% positive response.
Main Application Making Beauty: Cosmetic Make-Up Application
Before Makeup After Makeup
Main Application Pre-Surgery Planning: Aesthetic Surgery Application
Pre Surgery Post Surgery
Future Scope Variation in weightage of ratios
Developing a priority-wise list of ratios.
Improvement in facial surgery suggestions Improve its accuracy and give more reliable results.
Considering other aspects Skin colour and texture.
Predicting Male Attractiveness
Thanks
