LNSMM: Eye Gaze Estimation With Local Network Share Multiview Multitask
This addresses eye gaze estimation for computer vision applications, representing an incremental improvement with a novel hybrid method.
The paper tackles simultaneous estimation of eye gaze points and directions by proposing a local sharing network and a multiview multitask learning framework, achieving state-of-the-art results on both indicators.
Eye gaze estimation has become increasingly significant in computer vision.In this paper,we systematically study the mainstream of eye gaze estimation methods,propose a novel methodology to estimate eye gaze points and eye gaze directions simultaneously.First,we construct a local sharing network for feature extraction of gaze points and gaze directions estimation,which can reduce network computational parameters and converge quickly;Second,we propose a Multiview Multitask Learning (MTL) framework,for gaze directions,a coplanar constraint is proposed for the left and right eyes,for gaze points,three views data input indirectly introduces eye position information,a cross-view pooling module is designed, propose joint loss which handle both gaze points and gaze directions estimation.Eventually,we collect a dataset to use of gaze points,which have three views to exist public dataset.The experiment show our method is state-of-the-art the current mainstream methods on two indicators of gaze points and gaze directions.