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Electrical & Computer Engineering Dept.
University of Patras, Patras, Greece
Evangelos Skodras
Nikolaos Fakotakis
Eyes represent the most distinctive landmarks of the human face
The position and movements of eyes are a significant source of information about the cognitive and affective state of human beings: eyes hold a key role in expressing interest, intention and attention
Importance of eye localization
University of University of PatrasPatras 1/131/13
3
A wide range of applications…
University of University of PatrasPatras
Non-glasses type 3D technologies Monitoring of drivers’ attention and vigilance
Visual attention analysis Interactive gaze-based interfaces for disabled people
2/132/13
4
Why hasn’t it been solved yet?
University of University of PatrasPatras
Completely unobtrusive eye localization with remotely located passive sensors (cameras)
Low resolution, inexpensive imaging devices (e.g. webcams, pinhole cameras)
Accurate and robust detection for human-machine interaction applications even in outdoor environments
Requirements posed:
Active sensors or dedicated
hardware
3/133/13
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Great variability in shape, color , eye state, head pose and ethnicity
Imaging conditions such as lighting and camera characteristics have a strong influence on how the eyes appear in the image
Low resolution or compressed images derived from inexpensive devices
Occlusions caused by hair, glasses, shadows and reflections
Challenges of remote passive sensors
University of University of PatrasPatras 4/134/13
What has been used1. Appearance based Methods
2. Feature based Methods
5/135/13
Incorporate eye knowledge implicitly They generally require a large amount of training data and powerful
non-linear algorithms in order to learn the high variability of eyes High accuracy in detecting the eye area Fail to provide an accurate detection of the eye center
Explicit use of the a priori eye knowledge in order to derive features such as shape, geometry, color and symmetry
• Geometrical information of edges• Parametric models (e.g. deformable templates)• Circular shape modeling (Hough, Starburst, Integrodifferential)• Symmetry operators• …
Proposed Method
University of University of PatrasPatras
o Based upon a synergy of color and radial symmetry
• Color distribution of the eye area is significantly different to its surroundings
• Iris and pupil are characterized by radially symmetric brightness patterns
Simple – no machine vision required
Efficient – 0.12 sec for a 240 x 320 color image (Matlab implementation)
Robust – occlusions, eye state, head pose, lighting changes
Accurate – for human computer interaction applications
Low-cost – using a standard webcam or pinhole camera
6/136/13
University of University of PatrasPatras
Eye Map Construction
Cb2 Cr2
Y Dilated
EyeMapC
Y
EyeMapC Eroded
EyeMapI
/
{ }2 21/
3 b r b rEyemapC C C C C= + + 𝐸𝑦𝑒𝑀𝑎𝑝𝐼= 𝑌⊕ 𝐵1𝐸𝑦𝑒𝑚𝑎𝑝𝐶 ⊝ 𝐵2
7/137/13
Gradient - based interest operator which detects points of high radial symmetry
Fast Radial Symmetry Transfrom
University of University of PatrasPatras
Determines the contribution each pixel makes to the symmetry of pixels around it
The contribution of every orientation is computed in a single pass over the image
8/138/13
Proposed System Overview
University of University of PatrasPatras
+
EyeMapI
Y Sluminance
SEyeMapI
9/139/13
University of University of PatrasPatras
Quantitative ResultsMethod
Accuracydn ≤ 0.05 dn ≤ 0.1 dn ≤ 0.25
Proposed Method
87.94 % 94.26 % 97.45 %
Yang et al. 68.72 % 81.56 % 96.45 %
Valenti et al.
56.54 % 84.69 % 97.92 %
MethodAccuracy
dn ≤ 0.05 dn ≤ 0.1 dn ≤ 0.25
Proposed Method
99.43 % 99.54 % 99.66 %
Yang et al. 98.16 % 98.39 % 99.08 %
Valenti et al.
89.81 % 98.59 % 99.88 %
GT
AV
Calt
ech
10/1310/13
Qualitative Results
University of University of PatrasPatras
Low computational complexity which allows real time performance with a proper C or hardware implementation
The use of color information (rarely investigated in literature for this purpose) contributes significantly for precise localization
High accuracy rates, outperforming existing methods, especially in low-resolution images
Future work Correlate the level of attention of a driver (gaze estimation) with
the danger of an impeding collision (vehicle detection and braking recognition).
Conclusions
12/1312/13
University of University of PatrasPatras 13/1313/13
Thank you for your Thank you for your attention!attention!
evskodras@upatras.gr
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