Inferring human gaze from appearance via adaptive linear regression

Feng Lu, Yusuke Sugano, Takahiro Okabe, Yoichi Sato
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引用次数: 133

Abstract

The problem of estimating human gaze from eye appearance is regarded as mapping high-dimensional features to low-dimensional target space. Conventional methods require densely obtained training samples on the eye appearance manifold, which results in a tedious calibration stage. In this paper, we introduce an adaptive linear regression (ALR) method for accurate mapping via sparsely collected training samples. The key idea is to adaptively find the subset of training samples where the test sample is most linearly representable. We solve the problem via l1-optimization and thoroughly study the key issues to seek for the best solution for regression. The proposed gaze estimation approach based on ALR is naturally sparse and low-dimensional, giving the ability to infer human gaze from variant resolution eye images using much fewer training samples than existing methods. Especially, the optimization procedure in ALR is extended to solve the subpixel alignment problem simultaneously for low resolution test eye images. Performance of the proposed method is evaluated by extensive experiments against various factors such as number of training samples, feature dimensionality and eye image resolution to verify its effectiveness.
通过自适应线性回归从外表推断人类的凝视
从人眼外观估计人眼注视的问题被认为是将高维特征映射到低维目标空间。传统的方法需要在眼睛外观流形上密集地获得训练样本,这导致了繁琐的校准阶段。在本文中,我们引入了一种自适应线性回归(ALR)方法,通过稀疏收集的训练样本进行精确映射。关键思想是自适应地找到训练样本的子集,其中测试样本是最线性可表示的。我们通过11 -优化来解决问题,并深入研究关键问题,寻求回归的最佳解决方案。本文提出的基于ALR的注视估计方法具有天然的稀疏性和低维性,能够使用比现有方法少得多的训练样本从不同分辨率的眼睛图像中推断出人类的注视。特别地,将ALR中的优化过程扩展到同时解决低分辨率测试眼图像的亚像素对齐问题。通过对训练样本数、特征维数和眼睛图像分辨率等因素的大量实验,验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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