Analysis of the Human Face

9/12/06

What are the Parameters?

• Length of Ear

• Interpupillary Distance

• Length of face

• Width of face– 2 measurements (ear to nose, both sides)

• Length of nose

Fibonacci numbers

• Fibonacci numbers are defined as a number which when divided by the number in the sequence before it, yields a number close to 1.618 – fixed at precisely 1.618 after the 13th in the series. – known as the "golden ratio."

• 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, …

GOLDEN RATIO = 1.618

• 233 / 144 = 1.618

• 377 / 233 = 1.618

• 610 / 377 = 1.618

• 987 / 610 = 1.618

• 1597 / 987 = 1.618

• 2584 / 1597 = 1.618L. Pisano Fibonacci

The Golden Ratio in the Human Face

• There are several golden ratios in the human face. – refers to the "ideal human face" determined by

scientists and artists.• The total width of the two upper front teeth /their height • Length of face / width of face,• Distance between the lips and where the eyebrows

meet /length of nose• Length of face / distance between tip of jaw and where

the eyebrows meet• Length of mouth / width of nose• Width of nose / distance between nostrils,• Distance between pupils / distance between eyebrows.

Other Parameters?

• Shadow

• Skin Reflectance

• Emotion?

• Others?

Data Collection

• Once we agree on measurements to be taken, each student will be assigned a number, will take face diagrams and a ruler.

• The class will be divided into two groups at random

• Each group will measure the facial parameters of the members of the other group and record the data on the face diagrams– This will provide multiple measurements on each

subject

How will we analyze the data?

• Measurements for each subject will be entered into an Excel spreadsheet and used to calculate:– Mean, deviation, variance and standard deviation

• Data will be combined to analyze– Group 1 vs. group 2– Male vs female– Other groupings?

• Differences between groups will be analyzed for significance using the student’s t test

What does the analysis mean?

• Have we introduced any biases into our measurement?

• Is what we have done an “experiment”?

• What conclusions can be drawn from our measurements? Can we extrapolate to the general population?

• What would you do to improve the quality and/or significance of the data?

How facial data are used

• The analysis of the model parameters for sample populations has revealed variations according to subject age, gender, skin type, and external factors (e.g., sweat, cold, or makeup).

• Users can edit the overall appearance of a face (e.g., changing skin type and age) or change small-scale features using texture synthesis (e.g., adding moles and freckles)

• Currently under intense development for counter-terrorism (face recognition)

For Next Time

• Please read Chapter 10 (page 145) in “The Art of Science”.

• For extra credit:– Obtain data from the internet or other source

on facial data from another population• E.g., distance between eyes as a function of

gender, age, etc.