year month day2015 01 16
Department

Mechanical and Structural System
Engineering

NameMohammad Amro Jamal ALDIBAJA

ID095106

Shinichi Suzuki
Supervisor Naoki Uchiyama

Ａｂｓｔｒａｃｔ

Title

Eye Shape Detection Methods Based on Eye Structure Modelingand Texture Analysis for Interface Systems（800 words）
Human-Computer Interaction systems (HCI) are being an attractive field for both researchers andcompanies in order to increase the human life quality and safety. This is because of the revolution in
producing mobile devices that have the capabilities to connect the internet and process information in avery short time frame. Eye is considered as a very promising interactive component because it can beemployed to achieve many tasks that usually done by hands as well as it has a faster behavior andresponse. For example, suppose that many products are randomly distributed on a production line andan operator wants to pick one of them by a manipulator robot. The conventional strategy is whether touse a joystick to control the robot’s clip or to move the courser on a PC screen and click on the desired
object. The robot will then generate the motion matrix to get the location of the object.
More sophisticated approach is to use eyes. The operator should only look at the object whether on
the screen or directly in the real world on the production line.

This work aims to develop and implement three methods to automatically extract the eye features. Theeye features are represented by the term “eye shape”. The eye shape consists of a set of points called eye
shape points. The eye shape points are distributed equally to represent the two eye corners and the twoeyelids. The eye shape identifies the eye state (open, nearly close and in-between). In addition, it describes
the appearance parameters such as scale, rotation angle and translation in eye images.
In order to detect the eye shape, Active Shape Model (ASM) is developed. The developments are donein terms of enhancing initial estimation of the eye shape in a testing eye image using an object recognition
technique. The object recognition technique tries to understand the topological eye structure in the testing
image. Based on this understanding, the eye state is estimated and an initial eye shape is suggested.
Searching on eye shape points is also enhanced by describing the local structure around the eye shapepoints using Principal Component Analysis (PCA). The capabilities of PCA in terms of generalization haveincreased the robustness of detecting the locations of eye shape points under different lighting conditions.
The improved ASM has outperformed the standard/original ASM with increasing accuracy andreducing detection time.
In the second approach, a totally new method in the domain of object shape detection is proposed. Log-polartransform (LPT) is utilized to convert the change of eye scales and rotations in Cartesian coordinate system
into translations. Consequently, the task of detecting x,y coordinates of an eye shape point is facilitated into
detecting only log coordinate in Log-Polar domain. LPT maps the pixels in equal size and hence thegeometrical details are preserved regardless the eye size/scale and state in the testing images.
In addition, the existence of eye components such as pupil, iris, eyelid and sclera makes the color
variation between the eye and skin very sharp. This variation is measured by applying PCA on RGB color
channels of the testing image against wide range of changing of lighting conditions. Based on theseproperties of eye representation by PCA and LPT, the eye shape is extracted regardless the change of scale,rotation, translation, eye states and lighting conditions.

This new method has performed very high accuracy with significantly reducing the detection timecompared to ASM and Deformable Template (DT). This means that it is very appropriate to be employed
in real time systems. Moreover, the design has proven that Log-Polar domain should be the baseline ofimplementing any eye shape detection technique.
In the third model design, as long as LPT has been proven as the initial step of any eye shape detection
technique, a recurrent neural network is trained to model the pixel profile in each row of Log-polar eye
images. Each pixel profile (row) contains a few pixels that represent the eye contour. The pixel values in a
pixel profile vary smoothly and there is no sudden or rapid change exists. Based on this fact, the pixel
value of the eye contour in a pixel profile is changed to create a sudden peak. The recurrent network istrained to predict the location of this peak based on some past outputs and previous inputs of pixel values.
By utilizing the above training approach for eye shape detection in a testing image, a Log-polar eye
image is described by a fixed number of pixel profiles. The locations of the eye contour in these profiles
are highlighted by creating peaks. The recurrent network is trained using different Log-polar eye images.
This strategy has significantly enhanced the recurrent network prediction ability because the recurrent
network has learned the patterns of pixel values as well as it has modeled the eye shape by learning thedifferent frequency of peak occurrences in an eye image.
The experimental results verified a robust performance of the proposed method based RNN
compared to ASM and DT performances. On the other hand, it emphasize that using neural network isvery promising for detecting the eye shape and more investigations should be conducted.