000204675 001__ 204675
000204675 005__ 20190317000100.0
000204675 0247_ $$2doi$$a10.1109/WACV.2015.91
000204675 037__ $$aCONF
000204675 245__ $$aTowards Convenient Calibration for Cross-Ratio based Gaze Estimation
000204675 269__ $$a2015
000204675 260__ $$c2015
000204675 336__ $$aConference Papers
000204675 520__ $$aEye gaze movements are considered as a salient modality for human computer interaction applications. Recently, cross-ratio (CR) based eye tracking methods have attracted increasing interest because they provide remote gaze estimation using a single uncalibrated camera. However, due to the simplification assumptions in CR-based methods, their performance is lower than the model-based approaches [8]. Several efforts have been made to improve the accuracy by compensating for the assumptions with subject- specific calibration. This paper presents a CR-based automatic gaze estimation system that accurately works under natural head movements. A subject-specific calibration method based on regularized least-squares regression (LSR) is introduced for achieving higher accuracy compared to other state-of-the-art calibration methods. Experimental results also show that the proposed calibration method generalizes better when fewer calibration points are used. This enables user friendly applications with minimum calibration effort without sacrificing too much accuracy. In addition, we adaptively fuse the estimation of the point of regard (PoR) from both eyes based on the visibility of eye features. The adaptive fusion scheme reduces accuracy error by around 20% and also increases the estimation coverage under natural head movements.
000204675 6531_ $$agaze estimation
000204675 6531_ $$aeye tracking
000204675 6531_ $$acalibration
000204675 6531_ $$across-ratio
000204675 700__ $$0246687$$g222228$$aArar, Nuri Murat
000204675 700__ $$0247424$$g234775$$aGao, Hua
000204675 700__ $$aThiran, Jean-Philippe$$g115534$$0240323
000204675 7112_ $$dJanuary 6-9, 2015$$cWaikoloa Beach, Hawaii, USA$$aIEEE Winter Conference on Applications of Computer Vision (WACV)
000204675 8564_ $$uhttps://infoscience.epfl.ch/record/204675/files/PID3471457.pdf$$zPreprint$$s493382$$yPreprint
000204675 909C0 $$xU10954$$0252394$$pLTS5
000204675 909CO $$qGLOBAL_SET$$pconf$$ooai:infoscience.tind.io:204675$$pSTI
000204675 917Z8 $$x222228
000204675 917Z8 $$x222228
000204675 917Z8 $$x222228
000204675 937__ $$aEPFL-CONF-204675
000204675 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000204675 980__ $$aCONF