Speaker: Mine-Quan Jing
National Chiao Tung University

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Application
Related techniques
 Segmentation
 Identification
 Recognition
Progress ( 目前進展 )
Systems Demo
 NTU,NCTU,NTHU,ACADMIA
SINICA

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Feature-Based Approach
 Skin color and face geometry
 Detection task is accomplished by
 Distance, angles and area of visual features
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Image-Based Approach
 As a general recognition system

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Feature-Based Approach
 Low-Level Analysis
 Segmentation of visual features
 Feature Analysis
 Organized the features into
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1. Global concept
2. Facial features
 Active Shape Models
 Extract the complex & non-rigid feature
Ex: eye pupil, lip tracking.

Low-Level Analysis:
of visual features
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Edges: (The most primitive feature)
 Trace a human head outline.
 Provide the information
 Shape & position of the face
 Edge operators
 Sobel
 Marr-Hildreth
 first and second derivatives of
Gaussians

Low-Level Analysis:
Segmentation of visual features
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The steerable filtering
1. Detection of edges
2. Determining the orientation
3. Tracking the neighboring edges
Edge-detection system
1.
2.
3.
1. Label the edge
2. Matched to a face model
3. Golden ratio height

1
 width 2
5

Low-Level Analysis:
Segmentation of visual features
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Gray information
 Facial feature ( eyebrows , pupils … )
Darker than their surrounding
 Application
 Search an eye pair
 Find the bright pixel (nose tips)
 Mosaic (pyramid) images

Segmentation of visual features:
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Color information
 Difference races?
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 Different skin color gives rise to a tight cluster in color space .
Color models
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Normalized RGB colors
A color histogram for a face is made r g
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R
R
R
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G
G
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G
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B
B b
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R
B
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G

B
 Comparing the color of a pixel with respect to the r and g.
Why normalized ? Brightness change

Low-Level Analysis:
Segmentation of visual features
 HSI color model
 For large variance among facial feature clusters [106].
• Extract lips, eyes, and eyebrows .
 Also used in face segmentation
 YIQ
 Color ’ s ranging from orange to cyan
• Enhance the skin region of Asians [29].
 Other color models
 HSV, YES, CIE-xyz …
 Comparative study of color space [Terrilon
188]

Low-Level Analysis:
Segmentation of visual features
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Color segmentation by color thresholds
 Skin color is modeled through
 Histogram or charts (simple)
 Statistical measures (complex)
 Ex:
• Skin color cluster can be represented as Gaussian distribution [215]
 Advantage of Statistical color model
 The model is updatable
 More robust against changes in environment

Low-Level Analysis:
Segmentation of visual features
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The disadvantage:
 Not robust under varying lighting condiction

Color based segmentation:
(Example)
The original image was taken from http://nn.csie.nctu.edu.tw/face-detection/ppframe.htm

Color based segmentation:
(Example)
The original image was taken from http://nn.csie.nctu.edu.tw/face-detection/ppframe.htm

Low-Level Analysis:
Segmentation of visual features
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Motion information a face is almost always moving
Disadvantages:
 What if there are other object moving in the background.
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1.
Four steps for detection
Frame differencing
2.
3.
4.
Thresholding
Noise removal
Locate the face http://ansatte.hig.no/~erikh/papers/hig98_6/node2.html#bevdet

Related techniques
Amount of pixels on each line in the motion image
A typical motion image
The original images were taken from http://ansatte.hig.no/~erikh/papers/hig98_6/node2.html#bevdet

Motion-Based segmentation:
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Motion estimation [126]
 People are always moving.
 For focusing of attention
 discard cluttered, static background
 A spatio-temporal Gaussian filter can be used to detect moving boundaries of faces.

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 a u (
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3 e
 a ( x
2  y
2  u
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 2 
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 2
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G ( x , y , t )

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Image-Based Approach
 Linear Subspace Methods
 Neural Networks
 Statistical Approaches

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The 5th International Conference on Automatic Face and Gesture
Recognition will take place 2002 in
Washington D.C., USA.