Face Detection: a Survey

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17 Νοε 2013 (πριν από 3 χρόνια και 10 μήνες)

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Face Detection:


a Survey

Speaker: Mine
-
Quan Jing

National Chiao Tung University

Outline


Application


Related techniques


Segmentation


Identification


Recognition


Progress (
目前進展
)


Systems Demo


NTU,NCTU,NTHU,ACADMIA
SINICA



The face detection
techniques


Feature
-
Based

Approach


Skin color

and
face geometry


Detection task is accomplished by



Distance, angles

and
area


of visual features



Image
-
Based

Approach


As a general recognition system



The face detection
techniques


Feature
-
Based

Approach


Low
-
Level Analysis


Segmentation

of visual features


Feature Analysis


Organized

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


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


The steerable filtering


1. Detection of edges

2. Determining the orientation

3. Tracking the neighboring edges


Edge
-
detection system

1.
1. Label the edge

2.
2. Matched to a face model

3.
3. Golden ratio


2
5
1
width
height


Low
-
Level Analysis:


Segmentation of visual features


Gray information


Facial feature ( eyebrows ,
pupils

)



Application


Search an eye pair


Find the bright pixel (nose tips)


Mosaic (pyramid) images

Darker

than their surrounding

Segmentation of visual features:
Color Based Segmentation


Color information


Difference races?


Different skin color gives rise to a
tight cluster in color
space
.


Color models


Normalized RGB colors


A color histogram for a face is made


Comparing the color of a pixel with respect to the r
and g.


B
G
R
B
b
B
G
R
G
g
B
G
R
R
r









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


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


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


Motion information


a face is almost
always moving


Disadvantages:


What if there are other object moving in
the background.



Four steps for detection

1.
Frame differencing

2.
Thresholding

3.
Noise removal

4.
Locate the face


http://ansatte.hig.no/~erikh/papers/hig98_6/node2.html#bevdet

Related techniques



Change Detector

A typical motion image



Amount of pixels on each line in
the motion image



The original images were taken from http://ansatte.hig.no/~erikh/papers/hig98_6/node2.html#bevdet

Motion
-
Based segmentation:




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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The face detection
techniques


Image
-
Based

Approach


Linear Subspace Methods


Neural Networks


Statistical Approaches


Related News


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