Transcript Face Detection: a Survey

Face Detection:
a Survey
Speaker: Mine-Quan Jing
National Chiao Tung University
Outline


Application
Related techniques
Segmentation
 Identification
 Recognition
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Progress (目前進展)
Systems Demo
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NTU,NCTU,NTHU,ACADMIA
SINICA
The face detection
techniques
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Feature-Based Approach
Skin color and face geometry
 Detection task is accomplished by
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
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Distance, angles and area
of visual features
Image-Based Approach
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As a general recognition system
The face detection
techniques
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Feature-Based Approach
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Low-Level Analysis
 Segmentation

of visual features
Feature Analysis
 Organized
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
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the features into
1. Global concept
2. Facial features
Active Shape Models
 Extract
the complex & non-rigid feature
Ex: eye pupil, lip tracking.
Low-Level Analysis:
Segmentation of visual features
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Edges: (The most primitive feature)
Trace a human head outline.
 Provide the information
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 Shape

& position of the face
Edge operators
 Sobel
 Marr-Hildreth
 first
and second derivatives of
Gaussians
Low-Level Analysis:
Segmentation of visual features

The steerable filtering
1. Detection of edges
2. Determining the orientation
3. Tracking the neighboring edges
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Edge-detection system
1.
2.
3.
1. Label the edge
2. Matched to a face model
3. Golden ratio
height 1  5

width
2
Low-Level Analysis:
Segmentation of visual features
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Gray information
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Facial feature ( eyebrows ,
pupils …)
Darker than their surrounding
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Application
 Search
an eye pair
 Find the bright pixel (nose tips)
 Mosaic (pyramid) images
Segmentation of visual features:
Color Based Segmentation
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Color information
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Difference races?
 Different
skin color gives rise to a tight cluster in color
space.
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Color models
 Normalized
R
RG  B
G
g 
RG  B
B
b 
RG  B
r 
RGB colors
 A color histogram for a face is made
 Comparing the color of a pixel with respect to the r
and g.
Why normalized ? Brightness change
Low-Level Analysis:
Segmentation of visual features
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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].
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Other color models
 HSV,
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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
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Skin color is modeled through
 Histogram
or charts (simple)
 Statistical measures (complex)
 Ex:
• Skin color cluster can be represented as Gaussian
distribution [215]
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Advantage of Statistical color model
 The
model is updatable
 More robust against changes in environment
Low-Level Analysis:
Segmentation of visual features

The disadvantage:

Not robust under varying lighting
condiction
Color based segmentation:
Skin model construction (Example)
The original image was taken from http://nn.csie.nctu.edu.tw/face-detection/ppframe.htm
Color based segmentation:
Skin model construction (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
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a face is almost always moving
Disadvantages:
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What if there are other object moving in
the background.
Four steps for detection
1.
2.
3.
4.
Frame differencing
Thresholding
Noise removal
Locate the face
http://ansatte.hig.no/~erikh/papers/hig98_6/node2.html#bevdet
Related techniques –
Change Detector
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
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 discard
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cluttered, static background
A spatio-temporal Gaussian filter can be
used to detect moving boundaries of
faces.
a
3
2
G ( x, y , t )  u ( ) e

 a ( x 2  y 2  u 2t 2 )
 2 1 2 
m( x, y, t )     2 2 G ( x, y, t )
u t 

The face detection
techniques
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Image-Based Approach
Linear Subspace Methods
 Neural Networks
 Statistical Approaches
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Related News
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The 5th International Conference
on Automatic Face and Gesture
Recognition will take place 2002 in
Washington D.C., USA.