ITE Technical Report
Online ISSN : 2424-1970
Print ISSN : 1342-6893
ISSN-L : 1342-6893
41.05 Multi-media Storage(MMS)/Consumer Electronics(CE)/Human Information(HI)/Media Engineering(ME)/Artistic Image Technology(AIT)
Session ID : MMS2017-38
Conference information

Host: Multi-media Storage(MMS)

Name : Multi-media Storage(MMS)/Consumer Electronics(CE)/Human Information(HI)/Media Engineering(ME)/Artistic Image Technology(AIT)

Date : February 20, 2017 - February 21, 2017

Eye gesture identification that robust to head posture change for mobile device
*Seiya NIII, Hiroki TAKAHASHI
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Keywords: Gesture, Eye gaze interface, HMM, Machine learning, Gaze tracking
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Abstract
Modern tablet devices with high performance and mobility have been widely used in daily life. The tablet computers are used in various environments that are a crowded commuter train, a kitchen for cooking and so on. In those environments, users cannot use their hands to operate a computer. Although it is effective to operate the computer by gaze, a precise gaze tracking device is required, such as an infrared camera or multiple cameras. This paper proposes a gaze gesture classification method with HMM even in a coarse gaze device.
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© 2017 The Institute of Image Information and Television Engineers
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