Female Facial Beauty

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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 ď ś Beauty Scoring System To develop a reliable facial beauty scoring system which scores faces based on their facial attractiveness.

ď ś Suggestions to improve facial beauty To provide some cosmetic and facial surgery suggestions, which will help the user to improve her facial beauty.

ď ś 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 ď ś Symmetric Faces tend to be more attractive than the asymmetric faces.

ď ś 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 ď ś Dr. Stephen Marquardt has developed a Beauty Mask for Ideal Beautiful Female Face. ď ś Warping of a face to this beauty mask makes it more beautiful.

+ Less Attractive Face

= Beauty Mask

More Attractive Face


Theory of Averageness

+

+

=


Research Methodology Training Module

Application Module

• Dataset Collection • Rating Collection

• Algorithm Design • Application Design


Training Dataset


Rating Method Internal Rating (out of 5)    

Based on the Pageant’s Judges’ Scores Winner  5 st 1 runner-Up  4 2nd Runner-Up  3

External Rating (out of 5)  Based on Ratings of our experts collected through survey (1,2,3,4 or 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


Training Algorithm Distance & Ratio Calculator

Image Database

Beauty Mask

Difference Calculator Scores Database

Correlation Analysis & Ratio Filter

Filtered Ratios


Algorithm Implementation Distance & Ratio Calculator RI

Suggestion Generator

Difference Calculator

RM Difference

Correlation Analysis & Ratio Filter 8.6

9.1 6.7

Scores

Min. Difference Max. Difference


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  Celebrities’ Faces 

Tested for 30 faces of celebrities and models.

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 %)

 Agreement with Averageness Hypothesis 

Average female face (of 64 faces) scored 9.22/10.


Test Results  Agreement with previous research results.

Our test results were very motivated and we got 87.5% positive response.

 

Beautiful

(9.49)

Good Looking Common Looking

(8.02)

(8.91)

Poor Looking

(4.51)

   

Asian Beauty Caucasian Beauty Unattractive

Ugly

(8.98)

(1.74)

(9.17)

(3.27)


Main Application ď ś Making Beauty: Cosmetic Make-Up Application

Before Makeup

After Makeup


Main Application ď ś 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


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