ABSTRACT

Information and communication technologies can have a key role in helping people with educational needs, considering both physical and cognitive disabilities. Replacing a keyboard or mouse, with eye-scanning cameras mounted on computers have become necessary tools for people without limbs or those affected with paralysis. This system acts as an interface between the person and the computer. It can be like an input device or eye tracking device. The idea behind this is pretty simple. When we look at a particular object the image of that object is formed on the eye. Similarly, if we gaze at a character, The camera scans the image of the character formed on the eye, allowing users to ‘type’ on a monitor as they look at the visual keyboard. The paper describes an input device, based on eye scanning techniques that allow people with severe motor disabilities to use gaze for selecting specific areas on the computer screen. The visual key system gives overall idea on how does this process goeson, also deals with the system architecture which includes calibration, image acquisition, segmentation, recognition, and knowledge base.

The paper mainly includes three algorithms, one for face position, for eye area identification, and for pupil identification which are based on scanning the image to find the black pixel concentration. Inorder to implement this we use software called dasher, which is highly appropriate for computer users who are unable to use a two handed keyboard. One-handed users and users with no hands love dasher. The only ability that is required is sight. Dashers along with eye tracking devices are used.

This model is a novel idea and the first of its kind in the making, which reflects the outstanding thinking of a human that he left no stone unturned.

presented by:
BY
SHIVARAM

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ABSTRACT
Information and communication technologies can have a key role in helping people with educational needs, considering both physical and cognitive disabilities. Replacing a keyboard or mouse, with eye-scanning cameras mounted on computers have become necessary tools for people without limbs or those affected with paralysis. This system acts as an interface between the person and the computer. It can be like an input device or eye tracking device. The idea behind this is pretty simple. When we look at a particular object the image of that object is formed on the eye. Similarly, if we gaze at a character, The camera scans the image of the character formed on the eye, allowing users to ‘type’ on a monitor as they look at the visual keyboard. The paper describes an input device, based on eye scanning techniques that allow people with severe motor disabilities to use gaze for selecting specific areas on the computer screen. The visual key system gives overall idea on how does this process goeson, also deals with the system architecture which includes calibration, image acquisition, segmentation, recognition, and knowledge base.

The paper mainly includes three algorithms, one for face position, for eye area identification, and for pupil identification which are based on scanning the image to find the black pixel concentration. Inorder to implement this we use software called dasher, which is highly appropriate for computer users who are unable to use a two handed keyboard. One-handed users and users with no hands love dasher. The only ability that is required is sight. Dashers along with eye tracking devices are used.
This model is a novel idea and the first of its kind in the making, which reflects the outstanding thinking of a human that he left no stone unturned.
1. INTRODUCTION
‘Vis-Key’ aims at replacing the conventional hardware keyboard with a ‘Visual Keyboard’. It employs sophisticated scanning and pattern matching algorithms to achieve the objective. It exploits the eyes’ natural ability to navigate and spot familiar patterns. Eye typing research extends over twenty years; however, there is little research on the design issues. Recent research indicates that the type of feedback impacts typing speed, error rate, and the user’s need to switch her gaze between the visual keyboard and the monitor.
2. THE EYE The eye is nearly a sphere with an average diameter of approximately 20mm.Three membranes – The Cornea &Sclera cover, the Choroids layer and the Retina – encloses the eye. When the eye is properly focused, light from an object is imaged on the retina. Pattern vision is afforded by the distribution of discrete light receptors over the surface of the Retina. There are two classes of receptors – Cones and Rods. The cones, typically present in the central portion of the retina called fovea is highly sensitive to color. The number of cones in the human eye ranges from6-7 millions. These cones can resolve fine details because they are connected to its very own nerve end. Cone vision is also known as Photopic or Bright-light vision. The rods are more in number when compared to the cones (75-150 million). Several rods are connected to a single nerve and hence reduce the amount of detail discernible by the receptors. Rods give a general overall picture of the view and not much inclined towards color recognition. Rod vision is also known as the Scotopic vision or Dim-light vision as illustrated in fig 2.1, the curvature of the anterior surface of the lens is greater than the radius of its posterior surface. The shape of the lens is controlled by the tension in the fiber of the ciliary body. To focus on distant objects , the controlling muscles cause the lens to be relatively flattened. Similarly to focus on nearer objects the muscles allow the lens to be thicker. The distance between the focal distance of the lens and the retina varies from 17 mm to 14 mm as the refractive power of the lens increases from its minimum to its maximum.
3. The Vis-Key System
The main goal of our system is to provide users suffering from severe motor disabilities (and that therefore are not able to use neither the keyboard nor the mouse) with a system that allows them to use a personal computer.
The Vis-Key system (fig 3.1) comprises of a High Resolution camera that constantly scans the eye in order to capture the character image formed on the Eye. The camera gives a continuous streaming video as output. The idea is to capture individual frames at regular intervals (say ¼ of a second). These frames are then compared with the base frames stored in the repository. If the probability of success in matching exceeds the threshold value, the corresponding character is displayed on the screen. The hardware requirements are simply a personal computer, Vis-Key Layout (chart) and a web cam connected to the USB port. The system design, which refers to the software level, relies on the construction, design and implementation of image processing algorithms applied to the captured images of the user.
4. System Architecture:
4.1. Calibration
The calibration procedure aims at initializing the system. The first algorithm, whose goal is to identify the face position, is applied only to the first image, and the result will be used for processing the successive images, in order to speed up the process. This choice is acceptable since the user is supposed only to make minor movements. If background is completely black (easy to obtain) the user’s face appears as a white spot, and the borders can be obtained in correspondence of a decrease in the number of black pixels. The Camera position is below the PC monitor; if it were above, in fact, when the user looks at the bottom of the screen the iris would be partially covered by the eyelid, making the identification of the pupil very difficult. The user should not be distant from the camera, so that the image does not contain much besides his/her face. The algorithms that respectively identify the face, the eye and the pupil, in fact, are based on scanning the image to find the black pixel concentration: the more complex the image is, the slowest the algorithm is too. Besides, the image resolution will be lower. The suggested distance is about 30 cm. The user’s face should also be very well illuminated, and therefore two lamps were posed on each side of the computer screen. In fact, since the identification algorithms work on the black and white images, shadows should not be present on the User’s face.
4.2. Image Acquisition:
The Camera image acquisition is implemented via the Functions of the AviCap window class that is part of the Video for Windows (VFW) functions. The entire image of the problem domain would be scanned every 1/30 of second .The output of the camera is fed to an Analog to Digital converter (digitizer) and digitizes it. Here we can extract individual frames from the motion picture for further analysis and processing.