eye feature detection -...
TRANSCRIPT
Eye Feature Detection
Rui Liu
PhD student M.E.
Final Project for Computer Vision
CONTENT
‣ 1 Introduction
‣ 1.1 Motivation
‣ 1.2 Goals
‣ 1.3 Experiment Devices and Environment
‣ 2 Methods & Algorithms
‣ 3 Experiment Results
‣ 4 Result Analysis
‣ 4.1 Assessment
‣ 4.2 Small Problems and Its Analysis
‣ 4.3 Moving Forward
1 Introduction
1.1 Motivation for Eye feature detection
(1) What could eye feature indicate?
reflecting psychological state
He sees the truth. It’s written all over our faces.
Indicating human intention
Maybe want to leave Maybe want to drink
(2) Motivation: detecting eye features for intuitive human-robot interaction
1.2 Goals
Pupil detection
pupil localization, pupil diameter/ mean area and its standard deviation in a certain time.
Blink detection
blinking status detection, blinking times and blinking rate in a certain time.
1.3 Experiment Devices and Environment
Head Mount
HD Camera
Pic Captured
(1) Devices
Common lab (400 lux)
(2) Environment
2 Methods & Algorithms
1.1 pupil detection
(1)Method:
a. Circle(d≈44)
b. Dark(Area≈1500)
c. Concentric
d. Region
Pupil
(2) Algorithm:
a. Circle detection: Two-stage Circular Hough Transform (‘imfindcircle’ )
b. Dark region detection: ‘regionprops’
d= 42Concentric
‣ (3) Parameter Calculation
pupil center (x, y)
Pupil Diameter d circle detection
Mean Area A = 𝜋𝑑2 𝑡
4
𝑡2𝑡1
/(𝑛𝑡2 − 𝑛𝑡1)
Standard Deviation of Pupil Diameter 𝜎 = (𝑑 𝑛𝑡 −𝑑 )2𝑡2𝑡1
𝑛𝑡2−𝑛𝑡1
1.2 blink detection
(1) Method:
Blinking pupil disappearing time >𝒕𝟎
OpenBlinking
‣ (2) Parameter Calculation
Blinking Rate Rate=N/(𝑡2-𝑡1)
Blinking Times N
3 Experiment Results
3.1 results in different situations
a. Beginning b. Blinking
c. Open d. Near Blinking
3.2 Result Video
3.3 Statistics Results
a. Eye status b. Blinking Rate (n/sec)
0 5 10 15 20-0.5
0
0.5
1
1.5
Time(s)
Eye S
tatu
s
Blinking Status
1 -- Blinking
0 -- Open
0 5 10 15 20-0.5
0
0.5
1
1.5
Time(s)
blin
k r
ate
(Tim
es/s
ec)
blink rate
d. Mean Pupil Area
0 5 10 15 20
1300
1400
1500
1600
Time(s)
Mean P
upil
Are
a(p
ixel2
)
Mean Pupil Area
0 5 10 15 200
20
40
60
Time(s)
Pupil
Dia
mete
r(pix
el)
Pupil Diameter
c. Pupil Diameter
0 5 10 15 200
1
2
3
Time(s)
Sta
ndard
Devia
tion o
f P
upil
Siz
e
Standard Deviation of Pupil Size
e. Standard Deviation of Pupil Size
4. Result Analysis
4.1 Assessments
Successful Rate for Pupil detection 99%
Successful Rate for Blink detection 100%
Successful Rate for Pupil Contour detection 90%
4.2 small problems and its analysis
(a) A few pupil contours could not be detected accurately when eye is
near blinking/open
Because
Open Num= 1
Rate= 0.469
—A(d)=1439.44
(d)= 2.314
Actual contour shape is ellipse which
is detected as 2 circles
(b) Pupil in some frames could not be detected near blinking/open.
Light intensity is too low near blinking/open.
Dark region detection : Failed
Because
4.3 Moving Forward
In the future, this project could be optimized in next aspects:
(1) The circle detection could be revised as circle/ellipse detection
(2) The sensitivities and thresholds of these algorithms could be
adjusted adaptively
Q&A
Thank You !
Rui Liu