Gaze estimation using convolutional neural networks

被引：0
|
作者：
Karmi, Rawdha [1 ]
Rahmany, Ines [1 ]
Khlifa, Nawres [2 ]
机构：
[1] Univ Kairouan, Fac Sci & Tech, Kairouan, Tunisia
[2] Univ Tunis El Manar, ISTMT, Lab Biophys & Technol Med, Tunis, Tunisia
来源：
SIGNAL IMAGE AND VIDEO PROCESSING | 2024年 / 18卷 / 01期
关键词：
Gaze estimation; Eye tracking; Deep learning; CNN;
D O I：
10.1007/s11760-023-02723-8
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Numerous investigations on gaze estimate techniques for analyzing human behavior have been made in recent years, the majority of which have focused on gaze tracking techniques. This article proposes a new method for gaze estimation. The proposed system is divided into three phases: (i) estimation of head position using convolutional neural networks (CNN) (VGG16, Resnet50, InceptionV3), (ii) detection of eyes area using Viola Jones' algorithm, and in phase (iii) gaze estimation using three different models: pre-trained CNN, CNN from scratch, as well as bilinear convolutional neural networks. Columbia gaze database is used in the validation experiments. The results obtained from our novel gaze estimation method exhibit significantly improved accuracy compared to the existing approaches in the same field, which are limited in number.
引用
收藏
页码：389 / 398
页数：10
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