Towards dynamic accessibility through soft gaze gesture recognition

It is difficult for some sets of users with physical disabilities to operate standard input devices such as a keyboard and mouse. Eye gaze technologies and more specifically gaze gestures are emerging to assist such users. There is a high level of inter and intra user variation in the ability to perform gaze gestures due to the high levels of noise with the gaze patterns. In this paper we use a novel fuzzy transfer learning approach in order to construct a fuzzy system for gaze gesture recognition which can be automatically adapted for different users and/or user groups. We show that the fuzzy system is able to recognise gestures across groups of both able bodied (AB) and disabled users through the use of a base of AB data surpassing an expert constructed classifier.