EyePhone

Eyephone Design:- :- 

One question we address in this paper is how useful is a cheap, ubiquitous sensor, such as the camera, in building HPI applications. We develop eye tracking and blink detection mechanisms based algorithms originally designed for desktop machines using USB cameras. We show the limitations of an off-the-shelf HCI technique  when used to realize a HPI application on a resource limited Mobile device such as the Nokia N810. The EyePhone algorithmic design breaks down into the following pipeline phases: 

1) An eye detection phase;

2) An open eye template creation phase;

3) An eye tracking phase;

4) A blink detection phase. 

Impact of Distance Between Eye and Tablet

Since in the current implementation the open eye template is created once at a fixed distance, we evaluate the eye tracking performance when the distance between the eye and the tablet is varied while using EyePhone. We carry out the measurements for the middle-center position in the display (similar results are obtained for the remaining eight positions) when the person is steady and walking. As expected, the accuracy degrades for distances larger than 18-20 cm (which is the distance between the eye and the N810 we currently use during the eye template training phase). The accuracy drop becomes severe when the distance is made larger (e.g., ∼45 cm). These results indicate that research is needed in order to design eye template training techniques which are robust against distance variations between the eyes and the phone.

Blink Detection 

To detect blinks we apply a thresholding technique for the normalized correlation coefficient returned by the template matching function as suggested in. However, our algorithm differs from the one proposed in. In the authors introduce a single threshold T and the eye is deemed to be open if the correlation score is greater than T, and closed vice versa. In the EyePhone system, we have two situations to deal with: the quality of the camera is not the same as a good USB camera, and the phone’s camera is generally closer to the person’s face than is the case of using a desktop and USB camera. Because of this latter situation the camera can pick up iris movements, i.e., the interior of the eye, due to Eyeball rotation.

EyePhone Overview 

As smartphones evolve researchers are studying new techniques to ease the human-mobile interaction. We propose EyePhone, a novel “hand-free” interfacing system capable of driving mobile applications/functions using only the user’s eyes movement and actions (e.g., wink). EyePhone tracks the user’s eye movement across the phone’s display using the camera mounted on the front of the phone; more specifically, machine learning algorithms are used to: i) track the eye and infer its position on the mobile phone display as a user views a particular application; and ii) detect eye blinks that emulate mouse clicks to activate the target application under view. We present a prototype implementation of EyePhone on a Nokia N810, which is capable of tracking the position of the eye on the display, mapping these positions to an application that is activated by a wink. At no time does the user have to physically touch the phone display.

Artificial Light Exposure For A Stationary Subject

In this experiment, the person is again not moving but in an artificially lit environment (i.e., a room with very low daylight penetration from the windows). We want to verify if different lighting conditions impact the system’s performance. The results, shown in Table 1, are comparable to the daylight scenario in a number of cases. However, the accuracy drops. Given the poorer lighting conditions, the eye tracking algorithm fails to locate the eyes with higher frequency. Daylight Exposure for Person Walking. We carried out an experiment where a person walks outdoors in a bright environment to quantify the impact of the phone’s natural movement; that is, shaking of the phone in the hand induced by the person’s gait. We anticipate a drop in the accuracy of the eye tracking algorithm because of the phone movement. This is confirmed by the results shown in Table 1, column 4. Further research is required to make the eye tracking algorithm more robust when a person is using the system on the move.

Conclusion

In this paper, we have focused on developing a HPI technology solely using one of the phone’s growing number of onboard sensors, i.e., the front-facing camera. We presented the implementation and evaluation of the EyePhone prototype. The EyePhone relies on eye tracking and blink detection to drive a mobile phone user interface and activate different applications or functions on the phone. Although preliminary, our results indicate that EyePhone is a promising approach to driving Mobile applications in a hand-free manner.