Performance Analysis of Face Detection Systems

1. Performance Analysis of Face Detection Systems
Melike Sennur Kocal (028354)
Onur Şenol (990406)

2. Face Detection
 Biometric id,Surveillance,Security.
 Too many variations of human face.
 Extracting the face.

3. Project Aim
 Analyse Performances of 3 Algorithms

4. Project Aim
 Analyse Performances of 3 Algorithms
 Face Detection Using Local SMQT Features and Split Up
SNoW Classifier
 Face Detection Using Efficient & Rank Deficient
 Simple & Accurate Color Face Detection Algorithm in
Complex Background

5. Project Aim
 For-Loop for many images
 Cropping the detected faces
 Place the cropped faces into an “output” folder
 Timer Function for total elapsed time.

6. Project Aim
 For-Loop for many images
 Cropping the detected faces
 Place the cropped faces into an “output” folder
 Timer Function for total elapsed time.
 COMPARISON

7. Face Databases
 HumanScan
 JAFFE
 ORL
 YALE
 Cohn Kanade
 FG-NET
 CVL

8. Face Databases
 HumanScan 1521 Grayscale
 JAFFE 213 Grayscale
 ORL 400 Grayscale
 YALE 165 Grayscale
 Cohn Kanade 180 Grayscale
 FG-NET 126 Color
 CVL 797 Color

9. Face Databases
 HumanScan 1521 Grayscale
 JAFFE 213 Grayscale
 ORL 400 Grayscale
 YALE 165 Grayscale
 Cohn Kanade 180 Grayscale
 FG-NET 126 Color
 CVL 797 Color
Efficient & Rank Deficient
SMQT Features
& Split up Snow Classifier
Simple & Accurate
Face Detection Algorithm
in Complex Background

10. Face Databases
 HumanScan 1521 Grayscale
 JAFFE 213 Grayscale
 ORL 400 Grayscale
 YALE 165 Grayscale
 Cohn Kanade 180 Grayscale
 FG-NET 126 Color
 CVL 797 Color
Efficient & Rank Deficient
SMQT Features
& Split up Snow Classifier
Simple & Accurate
Face Detection Algorithm
in Complex Background
(2x6) + (1x2) = 14 test runs

11. Name & Color Conversions
 Name & Color Conversions
 Freeware Software “IrfanView”

12. Local SMQT & Split Up Snow Classifier
 Main.m
 Facefind.m
 Facefind.dll
 Plotbox.m
 Plotsize.m

13. Input Image

14. Convert
From RGB to GrayScale

15. Convert to Double

16. Sending image to facefind.dll

17. Draw Frames

19. main.m

20. main.m
Facefind.m
Image as double

21. main.m
Facefind.mFacefind.dll
Image as double
X1,y1,x2,y2

22. main.m
Facefind.m
Plotbox.m
Facefind.dll
Image as double
X1,y1,x2,y2 X1,y1,x2,y2

23. main.m
Facefind.m
Plotbox.m Plotsize.m
Facefind.dll
Image as double
X1,y1,x2,y2 X1,y1,x2,y2
Min&Max face sizes

24. main.m
Facefind.m
Plotbox.m Plotsize.m
Facefind.dll
Image as double
X1,y1,x2,y2 X1,y1,x2,y2
Min&Max face sizes

25. Modification
Timer Starts
Timer Stops

26. Modification
Extension Definition for input images
Converting int to str
Path i.e:150.pgm
150 + ‘pgm’

27. Modification
Same as
the original code

28. x1 x2
y1
y2
Height
output(4)-output(3)
Width
output(2)-output(1)
Output(1) Output(2)
Output(4)
Output(3)
X
Y
Modification

29. Modification
Converted
to integers
Conversion of coordinates for imcrop() function

30. Modification
Image input (again)
Rect array
Getting cropped image into matrix tem

31. Modification
Concatenation i.e; ‘150’+’jpg’
Cropped image as temp matrix
Full output path (Directory)
JPEG COMPRESSION

32. ex01.m
Facefind.m
Convertion to x,y,w,h
Facefind.dll
Image as double
x1,y1,x2,y2 X1,y1,x2,y2
rect=[x,y,w,h]
imcrop(image,rect)
image
Cropped image

33. Run DEMO
 Demonstrating Modified Code on MATLAB

34. Efficient & Rank Deficient

36. Image

37. Image Image & Threshold
Fdmex()

38. Image Image & Threshold
Fdmex()
Image & Threshold

39. Image Image & Threshold
Fdmex()
x,y,width,height
Image & Threshold
S-array

40. Image Image & Threshold
Fdmex()
x,y,width,height
Image & Threshold
S-arrayRectangle()

41. Image Image & Threshold
Fdmex()
x,y,width,height
Image & Threshold
S-arrayRectangle()

42. Efficient & Rank Deficient
Modification
Behaves like a sort of sensitivity
Get the image
Extension Definition

43. Efficient & Rank Deficient
Modification
Get coordinates for each face
X1,Y1,W,H

44. Efficient & Rank Deficient
Modification

45. Image Image & Threshold
Fdmex()
x,y,width,height
Image & Threshold

46. Run DEMO
 Demonstrating Modified Code on MATLAB

47. Simple & Accurate
 Lighting Compensation
 Extracting Skin
 Removing Noise
 Finding Skin Color Blocks
 Checking Face Criterions

48. Simple & Accurate Modification

49. Simple & Accurate Modification

50. Run DEMO
 Demonstrating Modified Code on MATLAB

51. Test Results
Database
# of
photos
Success (%) Seconds
ORL 400 339 84.75 4,83
YALE 165 142 86.06 2,06
JAFFE 213 213 100 8,67
COHN KANADE 180 176 97.7 32,7
HUMAN SCAN 1521 918 60.355 105,05
FG-NET 126 121 96.031 5,92
AVERAGE 87.50 26,54
Efficient & Rank Deficient Approach

52. Test Results
Database
# of
photos
Success (%) Seconds
ORL 400 400 100 135,9
YALE 165 165 100 39,462
JAFFE 213 211 99.06 1095,77
COHN KANADE 180 164 91.11 3095,838
HUMAN SCAN 1521 1490 97.96 11159,83
FG-NET 126 126 100 452,06
AVERAGE 98.02 % 2663,14
Using Local SMQT Features & Split Up SNoW Classifier

53. Test Results
Database # of photos
# of
cropped
photos
# of
correct
photos
(%) Seconds
FG-NET 126 119 75 59.5 102.83
CVL 797 752 575 72.1 4283.835
A Simple & Accurate
Face Detection Algorithm in Complex Background

54. Discussion of Results

55. Discussion of Results

56. Discussion of Results

57. Discussion of Results

58. Discussion of Results

59. Conclusion
Local SMQT & Split Up Snow
Classifier
Efficient & Rank Deficient
% 98 SUCCESS % 87.5 SUCCESS
2663,14 seconds 26,54 seconds

60. Conclusion
Local SMQT & Split Up Snow
Classifier
Efficient & Rank Deficient
% 98 SUCCESS % 87.5 SUCCESS
2663,14 seconds 26,54 seconds
CVL FG-NET
% 98 SUCCESS % 87.5 SUCCESS
4238,835 seconds 102,83 seconds
5.3 secondsimage 0.8 secondsimage