静态图像的凝视方向估计

Krystian Radlak, M. Kawulok, B. Smolka, Natalia Radlak
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引用次数: 9

摘要

提出了一种基于静态图像的多层次凝视方向识别算法。该解决方案包括三个阶段:(i)使用结合支持向量机验证器的多级椭圆检测器进行瞳孔定位;(ii)使用混合投影函数计算眼睛边界盒定位;(iii)使用支持向量机和随机森林进行凝视方向分类。该方法已在Eye-Chimera数据库上进行了测试,取得了令人满意的结果。大量的测试表明,眼边界盒定位使我们能够在眼睛定位和凝视方向分类方面获得高度准确的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Gaze direction estimation from static images
This study presents a novel multilevel algorithm for gaze direction recognition from static images. Proposed solution consists of three stages: (i) eye pupil localization using a multistage ellipse detector combined with a support vector machines verifier, (ii) eye bounding box localization calculated using a hybrid projection function and (iii) gaze direction classification using support vector machines and random forests. The proposed method has been tested on Eye-Chimera database with very promising results. Extensive tests show that eye bounding box localization allows us to achieve highly accurate results both in terms of eye location and gaze direction classification.
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