### Document

```Label the group photo
locate and identify faces and label them
Label the group photo
locate and identify faces and
label them
Ramona Ciulpan
Webmaster
Label the group photo
locate and identify faces and
label them
Kornel Toth
SVM, Database
Label the group photo
locate and identify faces and
label them
Mircea Focşa
PPT Presentation
Label the group photo
locate and identify faces and
label them
Krisztian Olle
Project manager
Project Description
Label the group photo - locate and
identify faces and label them
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Input group photo ( for example 10 people)
Segment it to isolate people/faces
Number the faces
Extract the faces
Build of library of faces
From photos of similar faces try to find that
person on the group photo
Face Detection
Finding faces is complicated?
Possible solution

Before the middle 90’s, the research attention was only
focused on single-face segmentation.
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Boosting
Neural Network
Template matching
Principal Component Analysis
Deformable feature-based template
Using skin color
Support Vector Machine
Our method here
Support Vector Machines algorithm
Minimize W(Λ) = - ΛT 1 + 1/2 Λ T D Λ
and
Subject to
ΛT y = 0
Λ-C1 ≤ 0
-Λ ≤0
Face detection (I)
• Create an images database
– 266 pictures: 150 faces + 116 non-faces
...
• Preprocessing
– Gray scale transformation
– Histogram equalization
– Adjust resolution to 30x40 pixel
• Training the SVM based on that 266 vectors,
using a polynomial kernel.
Face detection (II)
• Moving over the input image with a
30x40 pixel sub window
• Histogram equalization of a sub window
• Classification by SVM
• Removing intersections
Face recognition
• Training the SVM based on the
people faces who want to
recognize
• Classifying the detected faces
• Labeling the known faces
Implementation (I)
 Input group photo
Isolate people / faces
Number the faces
Implementation (II)
 Input group photo
 Isolate people / faces
 Number the faces
Implementation (III)
 Extract the faces
Implementation (IV)
 Build of library of
faces
Implementation (V)
Train the SVM with new set of vectors
 Label the faces
Results
Image name Resolution
# of
faces
# of
tests
# of
found faces
Time
(sec.)
False
Classific.
csoport.pgm
600x398
15
9600
13
11.45
6
team2.pgm
700x465
4
13020
4
15.077
0
team3.pgm
600x398
4
9600
4
14.671
0
team31.pgm
500x331
4
6700
4
10.499
0
team4.pgm
500x331
4
6700
4
10.515
0
team41.pgm
400x265
4
4240
4
5.984
0
test5.pgm
500x332
5
6700
4
9.937
1
Examples
Future Plans
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Multi-resolution image pyramid
Better face databases
Better face recognition databases
Improve the speed
Improve the masking technique
Thank You!
How many faces ?
4
3
5
6
2
7
1
8
11
10
9
References
• Open Source Computer Vision Library Reference Manual
http://developer.intel.com/
• Guodong Guo, Stan Z. Li, and Kapluk Chan: “Face
Recognition by Support Vector Machines” Proceeding of
Fourth IEEE International Conference on Automatic Face and
Gesture Recognition, 2000 Grenoble, France.
• Edgar Osuna, Robert Freund: “Training Support Vector
Machines: an Application to Face Detection”. Proceeding
of CVPR’97, 1997 Puerto Rico
• The Face Detection Homepage
http://home.t-online.de/home/Robert.Frischholtz/face.htm
```