Evaluation of Appearance-Based Methods and Implications for Gaze-Based Applications

Zhang, Xucong; Sugano, Yusuke; Bulling, Andreas

doi:10.1145/3290605.3300646

Cited by 93 publications

(65 citation statements)

References 81 publications

(95 reference statements)

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“…The functions E and D in our transforming encoderdecoder architecture can be implemented with any CNN architecture. We select the DenseNet CNN architecture [18] both for our architecture as well as for our reimplementation of state-of-the-art methods [26,55]. The latent codes z a , z g , and z h are defined to have dimensions (64), (3 × 2), and (3 × 16) respectively.…”

Section: Neural Network Configurationsmentioning

confidence: 99%

“…Ours vs Polynomial fit to PoR. In [55], Zhang et al fit a 3rd order polynomial function to correct initial pointof-regard (PoR) estimates from a person-independent gaze CNN. We train a DenseNet CNN for this purpose and intersect the predicted gaze ray (defined by gaze origin and direction in 3D with respect to the original camera) with the z = 0 plane to acquire an estimate for PoR.…”

Section: Comparison With State-of-the-artmentioning

confidence: 99%

See 1 more Smart Citation

Few-Shot Adaptive Gaze Estimation

Park

Mello²,

Molchanov³

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

167

128

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Inter-personal anatomical differences limit the accuracy of person-independent gaze estimation networks. Yet there is a need to lower gaze errors further to enable applications requiring higher quality. Further gains can be achieved by personalizing gaze networks, ideally with few calibration samples. However, over-parameterized neural networks are not amenable to learning from few examples as they can quickly over-fit. We embrace these challenges and propose a novel framework for Few-shot Adaptive GaZE Estimation (FAZE) for learning person-specific gaze networks with very few (≤ 9) calibration samples. FAZE learns a rotation-aware latent representation of gaze via a disentangling encoder-decoder architecture along with a highly adaptable gaze estimator trained using meta-learning. It is capable of adapting to any new person to yield significant performance gains with as few as 3 samples, yielding state-of-the-art performance of 3.18 • on GazeCapture, a 19% improvement over prior art.

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Section: Neural Network Configurationsmentioning

confidence: 99%

Section: Comparison With State-of-the-artmentioning

confidence: 99%

Few-Shot Adaptive Gaze Estimation

Park

Mello²,

Molchanov³

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

167

128

View full text Add to dashboard Cite

show abstract

“…Solutions to these issues can be both use-case and hardware dependent. One possible general approach to help overcome these limitations however is to augment our infrared-illumination-based hybrid approach with gaze-estimation techniques that use natural light [35,36,58]. In a dual-model system, situations when infrared eye features are difficult to localize the less accurate methods that use natural light can be used to improve the range of mobile eye-tracking-based applications.…”

Section: Discussionmentioning

confidence: 99%

Hybrid Eye-Tracking on a Smartphone with CNN Feature Extraction and an Infrared 3D Model

Brousseau

Rose

Eizenman

2020

Sensors

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This paper describes a low-cost, robust, and accurate remote eye-tracking system that uses an industrial prototype smartphone with integrated infrared illumination and camera. Numerous studies have demonstrated the beneficial use of eye-tracking in domains such as neurological and neuropsychiatric testing, advertising evaluation, pilot training, and automotive safety. Remote eye-tracking on a smartphone could enable the significant growth in the deployment of applications in these domains. Our system uses a 3D gaze-estimation model that enables accurate point-of-gaze (PoG) estimation with free head and device motion. To accurately determine the input eye features (pupil center and corneal reflections), the system uses Convolutional Neural Networks (CNNs) together with a novel center-of-mass output layer. The use of CNNs improves the system’s robustness to the significant variability in the appearance of eye-images found in handheld eye trackers. The system was tested with 8 subjects with the device free to move in their hands and produced a gaze bias of 0.72°. Our hybrid approach that uses artificial illumination, a 3D gaze-estimation model, and a CNN feature extractor achieved an accuracy that is significantly (400%) better than current eye-tracking systems on smartphones that use natural illumination and machine-learning techniques to estimate the PoG.

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“…The eye-gaze tracking technology has matured and become inexpensive. This technology can be built into computers, HMDs, and mobile devices [8]. The direction of the eye-gaze is a crucial and augmented input medium/control modality through which humans express socially relevant information, particularly the individual's cognitive state [7,20,21].…”

Section: Eye-gaze Trackingmentioning

confidence: 99%

“…Nowadays, eye tracking has matured and become an important research topic in computer vision and pattern recognition, because human gaze positions and movements are essential information for many applications ranging from diagnostic to interactive ones [3][4][5][6]. Eye tracking equipment, either worn on the body (head-mounted) [7] or strategically located in the environment [8], is a key requirement of gaze-based applications. In contrast, recent advances in the applications of head-mounted displays (HMDs) for virtual reality have driven the development of HMD-integrated eye-tracking technology.…”

Section: Introductionmentioning

confidence: 99%

GazeGuide: An Eye-Gaze-Guided Active Immersive UAV Camera

et al. 2020

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Over the years, gaze input modality has been an easy and demanding human–computer interaction (HCI) method for various applications. The research of gaze-based interactive applications has advanced considerably, as HCIs are no longer constrained to traditional input devices. In this paper, we propose a novel immersive eye-gaze-guided camera (called GazeGuide) that can seamlessly control the movements of a camera mounted on an unmanned aerial vehicle (UAV) from the eye-gaze of a remote user. The video stream captured by the camera is fed into a head-mounted display (HMD) with a binocular eye tracker. The user’s eye-gaze is the sole input modality to maneuver the camera. A user study was conducted considering the static and moving targets of interest in a three-dimensional (3D) space to evaluate the proposed framework. GazeGuide was compared with a state-of-the-art input modality remote controller. The qualitative and quantitative results showed that the proposed GazeGuide performed significantly better than the remote controller.

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Evaluation of Appearance-Based Methods and Implications for Gaze-Based Applications

Cited by 93 publications

References 81 publications

Few-Shot Adaptive Gaze Estimation

Few-Shot Adaptive Gaze Estimation

Hybrid Eye-Tracking on a Smartphone with CNN Feature Extraction and an Infrared 3D Model

GazeGuide: An Eye-Gaze-Guided Active Immersive UAV Camera

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