This document presents a system for detecting driver fatigue based on eye tracking. It begins with a literature review on existing eye tracking and fatigue detection techniques. It then describes the proposed system with a block diagram showing the workflow, which includes face and eye detection, image preprocessing, edge detection, and eye state analysis by measuring eyelid distance and template matching. Experimental results are shown for ROI extraction accuracy and system performance on detecting open and closed eyes over multiple tests. Future work ideas are provided to improve the system.

4. Driver’s Fatigue Detection based on
Eye Tracking
FALGUNI ROY
BSSE 0230

5.  Literature Review
 Proposed System
 Experimental Result

6. Literature Review

7. LITERATURE REVIEW
 Dynamic Template Matching
 Distance of Eyelid
 Analyzing the Eye State
 Facial Analysis Techniques

8. PROPOSED SYSTEM

9. PROPOSED SYSTEM IN BLOCK DIAGRAM

10. SYSTEM WORKFLOW

11. ROI EXTRACTION

Face detection
Face Detection
Face Area Cropping

12. ROI EXTRACTION(CONT’D)

Eye Detection
Eye Detection &
Cropping

13. IMAGE PRE-PROCESSING
Histogram Equalize
Median Filter

14. EDGE DETECTION

15. EYE STATE ANALYSIS

Measure the distance of eyelid

Match the eye image with predefined template

16. MEASURE THE DISTANCE OF EYELID
x = (image_width/8)
y = (image_height/3)
height = (3* image_height)/5
width = (3* image_width)/5

17. MATCH THE EYE IMAGE WITH PREDEFINED TEMPLATE
 Use Euclidian Distance
 If m =< threshold then we assume it as a close eye and in fatigue state otherwise discard the image
 Here m = matching amount between template and image

18. Experimental Result

19. EXPERIMENTAL SETUP
 Programming language: Java, javaCV, OpenCV
 EclipseVersion:eclipse-jee-helios-win32
 OS: 32 bit

20. LINEAR REPRESENTATION OF THE SYSTEM WITH OPEN EYE
Image pre-processing & edge detection
Eye state analysis
Eye is open and
discards the image and
continues with the next
image

21. LINEAR REPRESENTATION OF THE SYSTEM WITH CLOSE EYE
Template matching & decision taking
If
the
5consecutive
close image then
the person is in
fatigue
Match
with
template
Not an open eye
image so pass it
for
template
matching

22. ROI EXTRACTION ONLY EYE DETECTION
Case
Length
Number Number of ROI
No.
of Video of frame face image
Extraction
with Accuracy
eye(Eye Detection)
TP/(total number
TP
FN
FP
TN
face image) %
Test 1 120 sec
295
295
188
102
5
0
64%
Test 2 90 sec
220
207
140
64
3
0
67%
Test 3 60 sec
145
145
88
50
7
0
61%
Test 4 30 sec
73
63
37
26
0
0
59%
Test 5 15 sec
36
36
21
13
2
0
58%

23. ROI EXTRACTION WITH FACE BEFORE EYE DETECTION
Case No.
Length
Number
Number
of Video
of frame
of
ROI Extraction (Face & Eye Accuracy
face Detection)
TP/(total
image
number
Face
TP
FN
FP
TN
image) %
Detection
Test 1
120 sec
295
295
242
227
13
2
0
77%
Test 2
90 sec
220
207
187
153
29
5
0
74%
Test 3
60 sec
145
145
135
110
22
3
0
76%
Test 4
30 sec
73
63
50
41
9
0
0
65%
Test 5
15 sec
36
36
34
25
7
2
0
69%
face

24. SYSTEM PERFORMANCE MEASUREMENT
Case
Number
No.
of
Open
face eye
Close
Eye
eye
(TP)
image
Detected Open eye
Close eye
Fatigue Detection
(%)
Open
Close
eye
TP
FN
TP
FN
eye
Test 1
295
165
130
145
82
122
23
69
13
84%
Test 2
207
109
98
100
53
87
13
41
12
77%
Test 3
145
60
85
43
67
38
5
60
7
89%
Test 4
63
45
18
33
12
27
6
9
3
75%
Test 5
36
14
22
10
15
6
4
12
3
80%
Average 81%

25. FUTURE WORKS
 Upgrade the system with eyeglass & both eyes
 Upgrade the system with a self light source
 Tracking eyes with the head rotation
 Update template with driver’s eyes dynamically

26. ANY QUESTION ??????