Eye/Head Tracking Technology to Improve HCI with iPad Applications

Abstract

In order to improve human computer interaction (HCI) for people with special needs, this paper presents an alternative form of interaction, which uses the iPad's front camera and eye/head tracking technology. With this functional nature/capability operating in the background, the user can control already developed or new applications for the iPad by moving their eyes and/or head. There are many techniques, which are currently used to detect facial features, such as eyes or even the face itself. Open source bookstores exist for such purpose, such as OpenCV, which enable very reliable and accurate detection algorithms to be applied, such as Haar Cascade using very high-level programming. All processing is undertaken in real time, and it is therefore important to pay close attention to the use of limited resources (processing capacity) of devices, such as the iPad. The system was validated in tests involving 22 users of different ages and characteristics (people with dark and light-colored eyes and with/without glasses). These tests are performed to assess user/device interaction and to ascertain whether it works properly. The system obtained an accuracy of between 60% and 100% in the three test exercises taken into consideration. The results showed that the Haar Cascade had a significant effect by detecting faces in 100% of cases, unlike eyes and the pupil where interference (light and shade) evidenced less effectiveness. In addition to ascertaining the effectiveness of the system via these exercises, the demo application has also helped to show that user constraints need not affect the enjoyment and use of a particular type of technology. In short, the results obtained are encouraging and these systems may continue to be developed if extended and updated in the future.

A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms

“…a) Estimating impact of head pose A user is positioned in front of the eye tracker while a video camera captures the position of the user's head simultaneously. The user's head pose in roll, pitch and yaw angles are obtained from the video using an appearance model [213] as shown in Fig.12. Then the user is asked to turn their head to specific fixed positions (in roll pitch yaw angles) and their gaze is tracked on the same interface again.…”

Object Acquisition and Selection in Human Computer Interaction Systems: A Review

“…Some commercially available eye gaze trackers (e.g. ERICA [28] and Tobii 1750 [8]) provide good accuracy and gaze resolution but their high cost is the major concern [29]. Although, gaze tracking can be performed by using simple webcams but these methods cannot beat high cost commercial gaze trackers in terms of accuracy and resolution.…”

“…The foremost problems related to cursor control using eye gaze are the issues of accuracy [89], fatigue [22], cursor stability [90] and response time [89]. Although some commercially available gaze trackers are available which can provide very good accuracy but their high cost [29] still restricts them to be easily used in HCI systems. When eye gaze tracking is used for object acquisition and dwell time for object selection, then keeping a cursor stable on small objects becomes very difficult even when best quality eye trackers are used [22].…”

SeleCon

“…Different forms of interaction have been proposed, such as speech recognition [10], face recognition [29], gaze/eye tracking [31], and hand gesture tracking [15]. However, despite this tremendous effort, we are still far from having a natural way to control and interact with devices.…”