triangle-based approach to the detection of human face
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Triangle-based approach to the detection of human face. March 2001 PATTERN RECOGNITION Speaker Jing. AIP Lab. Outline. Introduction Segmentation of potential face regions Face verification Experimental results and discussion. Introduction 1/3. - PowerPoint PPT PresentationTRANSCRIPT
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Triangle-based approach to the detection of human face
March 2001 PATTERN RECOGNITION
Speaker Jing. AIP Lab
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Outline Introduction Segmentation of potential face regions Face verification Experimental results and discussion
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Introduction 1/3
Given a still or video image, detect and localize an unknown number of faces
– Security mechanism (replace key, card,passwd)– Criminology (find out possible criminals)– Content-based image retrieval – video coding – video conferencing – Crowd( 大眾 ) surveillance and intelligent human-comput
er interfaces.
Applications
Problem
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Introduction 2/3
Requirement
* achieve the task regardless of
- illumination, orientation, and camera distance
Why difficult ?
Human face is a dynamic objectHigh degree of variability in appearance ( 面孔的多變性 )
* Speedy and correct detection rate
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Introduction 3/3
Drawbacks of the papers until now– Free of background– Cannot detect a small face ( < 50 *
50)– Cannot detect multiple face ( >3)– Cannot handle the defocus and noise– Cannot conquer the partial occlusion
of mouth or wear sunglasses– Cannot detect a face of side view
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A classified algorithms
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Begin the method
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Overview of the system1. Form 4-connected components2. Find the center for each one
1. Search any 3 center that form an isosceles or right triangle
1. Normalize the size of potential face regions
1. Calculate the weight by mask function
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Segmentation 4 step for segmenting the potential
face– Convert the input image to a binary image– Find the blocks using 4-connected
component– Search the triangle– Clip the satisfy triangle region
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Step1: Convert the image RGB Color Image
– Eliminating the hue and saturation – Gray-level binary image
– Remove noise using opening operation– Eliminate holes by the closing operation
Gray-level < T are labelled as blackGray-level > T are white
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Step 2:Form the blocks & Searching triangle Form the blocks by using 4-connected
components algorithm
Locate the center of each block
Searching the triangle– Frontal view (isosceles triangle)
– Side view (right triangle)
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Step 3: Frontal view (isosceles triangle) Isosceles triangle: D(ij)=D(jk)
Matching rule:
i k
j
),max(25.0|| cbcb
),max(25.0|| cbab Eye to mouth
mouth to mouth
a
b c
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Clipping the region 2/4
X1=X4=Xi – 1/3 dX2=X3=Xk + 1/3 dY1=Y2=Yi + 1/3 dY3=Y4=Yj – 1/3 d
Xi,Yi d Xk,Yk
Xj,Yj
x1 x2
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Side view (right triangle) 3/4 Right triangle
Matching Rules: (25% derivation)1. 0.4 a < | a-c | < 0.6 a2. 0.13 a < | a-b | < 0.19 a3. 0.29 a < | b-c | < 0.44 a
i j
k
3
2 1a
b
c
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Clipping the region 4/4
i
j
k
d
1.2d
d/4
d
d/6
X1=X4=Xi-d/6X2=X3=Xi+1.2dY1=Y2=Yi+d/4Y3=Y4=Yi-d
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Speedup of searching
How many triangles ?
If the mouth & right eye are already known, => the left eye should be located in the near
area.
nC3
i
j
k
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Face verification
3 steps in verificationStep1: Normalization the potential facial areas
– 60 * 60 pixels
Step 2: Calculating the weight by masking function
Step 3:Verification by thresholding the weight
Question 1 . How to generate the face mask ?
Question 2 . How to calculate the weight ?
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Question 1 . How to generate the face mask ?
Read the 10 binary training masks Add the corresponding entries Binarized the added mask
Ex:
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Question 2 . How to calculate the weight
Eye and mouth are labeled as black, others as white– If the pixels in the P is equal to T
• Both Black: Weight + 6• Both White : Weight + 2
– White in P and black in T• Weight –2
– White in T and black in P• Weight - 4
P: potential facial regionT: Training mask
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Verification For each potential facial regions
– Threshold value is given for decision making• Front view => 4000 < threhold < 5500• Side view => 2300 < threhold < 2600
Finally, eliminate the regions that– Overlap with the chosen facial region
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Result—frontal view
Original Binary Isosceles triangle
clipping Normalized
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Result – Side View
Original Binary Isosceles triangle
clipping Normalized
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Experimental results
500 test images– included 450 different persons– 600 faces that are used
11 faces cannot be found correctly98% success rate
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Experiment result
Scaling: 5*5 to 640*480
Light condition
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Experiment Result
Distinct position
Defocus face
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Experiment Result
Changed expressions
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Experiment Result
Noise Occlusion Sunglasses
cartoon Chinese doll
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Experiment Result
2.5 sec 28 sec
Target machine: PII 233 PC
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Experiment ResultMulti-faces and video stream
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Experiment Result
False cases
Too Dark Right eye being occluded
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Conclusion
Manage different sizes, changed light conditions, varying pose and expression
Cope with partial occlusion problem Detect a side-view face In the future, using this algorithm
for solving face recognition problem
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My opinions The processing time depend on the
complexity of the image. Real-time requirement was
unachievable. (some images need 28 sec to process)