scholarly journals Head pose‐free gaze estimation using domain adaptation

2021 ◽  
Author(s):  
Byungtae Ahn ◽  
Minseok Seo ◽  
Dong‐Geol Choi
Keyword(s):  
Domain Adaptation ◽  
Gaze Estimation ◽  
Head Pose

scholarly journals Real-time gaze estimation via pupil center tracking

2018 ◽  
Vol 9 (1) ◽  
pp. 6-18 ◽  
Author(s):  
Dario Cazzato ◽  
Fabio Dominio ◽  
Roberto Manduchi ◽  
Silvia M. Castro
Keyword(s):  
Real Time ◽  
Learning Algorithm ◽  
Natural Environments ◽  
Gaze Estimation ◽  
Head Pose ◽  
Data Set ◽  
Gaze Tracking ◽  
Illumination Changes ◽  
Wide Range ◽  
Estimation System

Abstract Automatic gaze estimation not based on commercial and expensive eye tracking hardware solutions can enable several applications in the fields of human computer interaction (HCI) and human behavior analysis. It is therefore not surprising that several related techniques and methods have been investigated in recent years. However, very few camera-based systems proposed in the literature are both real-time and robust. In this work, we propose a real-time user-calibration-free gaze estimation system that does not need person-dependent calibration, can deal with illumination changes and head pose variations, and can work with a wide range of distances from the camera. Our solution is based on a 3-D appearance-based method that processes the images from a built-in laptop camera. Real-time performance is obtained by combining head pose information with geometrical eye features to train a machine learning algorithm. Our method has been validated on a data set of images of users in natural environments, and shows promising results. The possibility of a real-time implementation, combined with the good quality of gaze tracking, make this system suitable for various HCI applications.

scholarly journals RGBD Based Gaze Estimation via Multi-Task CNN

2019 ◽  
Vol 33 ◽  
pp. 2488-2495 ◽  
Author(s):  
Dongze Lian ◽  
Ziheng Zhang ◽  
Weixin Luo ◽  
Lina Hu ◽  
Minye Wu ◽  
...  
Keyword(s):  
Large Scale ◽  
Image Synthesis ◽  
Position Estimation ◽  
Point Estimation ◽  
Head Pose Estimation ◽  
Depth Image ◽  
Eye Position ◽  
Gaze Estimation ◽  
Head Pose ◽  
Gaze Tracking

This paper tackles RGBD based gaze estimation with Convolutional Neural Networks (CNNs). Specifically, we propose to decompose gaze point estimation into eyeball pose, head pose, and 3D eye position estimation. Compared with RGB image-based gaze tracking, having depth modality helps to facilitate head pose estimation and 3D eye position estimation. The captured depth image, however, usually contains noise and black holes which noticeably hamper gaze tracking. Thus we propose a CNN-based multi-task learning framework to simultaneously refine depth images and predict gaze points. We utilize a generator network for depth image generation with a Generative Neural Network (GAN), where the generator network is partially shared by both the gaze tracking network and GAN-based depth synthesizing. By optimizing the whole network simultaneously, depth image synthesis improves gaze point estimation and vice versa. Since the only existing RGBD dataset (EYEDIAP) is too small, we build a large-scale RGBD gaze tracking dataset for performance evaluation. As far as we know, it is the largest RGBD gaze dataset in terms of the number of participants. Comprehensive experiments demonstrate that our method outperforms existing methods by a large margin on both our dataset and the EYEDIAP dataset.

Discriminative Robust Head-Pose and Gaze Estimation Using Kernel-DMCCA Features Fusion

2020 ◽  
Vol 14 (01) ◽  
pp. 107-135
Author(s):  
Salah Rabba ◽  
Matthew Kyan ◽  
Lei Gao ◽  
Azhar Quddus ◽  
Ali Shahidi Zandi ◽  
...  
Keyword(s):  
Dimensional Space ◽  
Accurate Estimation ◽  
Gaze Estimation ◽  
Head Pose ◽  
Facial Hair ◽  
Features Fusion ◽  
Higher Dimensional ◽  
Wide Range ◽  
Feature Information ◽  
Improving Accuracy

There remain outstanding challenges for improving accuracy of multi-feature information for head-pose and gaze estimation. The proposed framework employs discriminative analysis for head-pose and gaze estimation using kernel discriminative multiple canonical correlation analysis (K-DMCCA). The feature extraction component of the framework includes spatial indexing, statistical and geometrical elements. Head-pose and gaze estimation is constructed by feature aggregation and transforming features into a higher dimensional space using K-DMCCA for accurate estimation. The two main contributions are: Enhancing fusion performance through the use of kernel-based DMCCA, and by introducing an improved iris region descriptor based on quadtree. The overall approach is also inclusive of statistical and geometrical indexing that are calibration free (does not require any subsequent adjustment). We validate the robustness of the proposed framework across a wide variety of datasets, which consist of different modalities (RGB and Depth), constraints (wide range of head-poses, not only frontal), quality (accurately labelled for validation), occlusion (due to glasses, hair bang, facial hair) and illumination. Our method achieved an accurate head-pose and gaze estimation of 4.8∘ using Cave, 4.6∘ using MPII, 5.1∘ using ACS, 5.9∘ using EYEDIAP, 4.3∘ using OSLO and 4.6∘ using UULM datasets.

scholarly journals Highly Accurate and Fully Automatic 3–D Head Pose Estimation and Eye Gaze Estimation Using RGB-–D Sensors and 3D Morphable Models

2018 ◽  
Author(s):  
Reza Shoja Ghiass ◽  
Denis Laurendeau
Keyword(s):  
Pose Estimation ◽  
Eye Gaze ◽  
Head Pose Estimation ◽  
Head Model ◽  
Gaze Estimation ◽  
Head Pose ◽  
Depth Data ◽  
Manual Intervention ◽  
Fully Automatic ◽  
Eye Gaze Estimation

This work addresses the problem of automatic head pose estimation and its application in 3D gaze estimation using low quality RGB--D sensors without any subject cooperation or manual intervention. The previous works on 3D head pose estimation using RGB--D sensors require either an offline step for supervised learning or 3D head model construction which may require manual intervention or subject cooperation for complete head model reconstruction. In this paper, we propose a 3D pose estimator based on low quality depth data, which is not limited by any of the aforementioned steps. Instead, the proposed technique relies on modeling the subject's face in 3--D rather than the complete head, which in turn, relaxes all of the constraints with the previous works. The proposed method is robust, highly accurate and fully automatic. Moreover, it does not need any offline step. Unlike some of the previous works, the method only uses depth data for pose estimation. The experimental results on the Biwi head pose database confirm the efficiency of our algorithm in handling large pose variations and partial occlusion. We also evaluate the performance of our algorithm on IDIAP database for 3D head pose and eye gaze estimation.

Gaze Estimation From Eye Appearance: A Head Pose-Free Method via Eye Image Synthesis

2015 ◽  
Vol 24 (11) ◽  
pp. 3680-3693 ◽  
Author(s):  
Feng Lu ◽  
Yusuke Sugano ◽  
Takahiro Okabe ◽  
Yoichi Sato
Keyword(s):  
Image Synthesis ◽  
Gaze Estimation ◽  
Head Pose
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