SalGaze: Personalizing Gaze Estimation using Visual Saliency

Chang, Zhuoqing; Martino, J. Matías Di; Qiu, Qiang; Espinosa, Steven; Sapiro, Guillermo

doi:10.1109/iccvw.2019.00148

Cited by 9 publications

(10 citation statements)

References 51 publications

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“…However, it is difficult to acquire enough labeled samples in practical applications. Collecting calibration samples in a user-unaware manner is an alternative solution [116], [117], [118].…”

Section: ) Calibration Via Domain Adaptationmentioning

confidence: 99%

“…Other studies investigate the relation between the gaze points and the saliency maps [102], [103]. Chang et al utilize saliency information to adapt the gaze cestimation algorithm to a new user without explicit calibration [116]. They transform the saliency map into a differentiable loss map that can be used to optimize the CNN models.…”

Section: ) Calibration Via Domain Adaptationmentioning

confidence: 99%

“…Eye image [26] - [42], [119], [113] [38], [47], [48], [95], [120] [40], [46], [51], [98], [100], [115], [121] [49], [50], [79], [52], [53], [80], [54] -Facial image - [35] [43], [64], [101] [66], [68], [104], [108] [5], [46], [69], [92], [99], [110], [111], [116], [118], [122] [49], [50], [67], [59], [61], [83], [62], [123], [78], [124], [63], [112], [82], [37], [107] [60]…”

Section: Featurementioning

confidence: 99%

“…[43], [64], [42], [101], [113], [119] [38], [47], [66], [68], [95], [104], [108], [120] [5], [36], [40], [46], [70], [71], [72], [98], [99], [100], [115], [116], [118], [121], [122] [82], [50], [49], [37], [59], [61], [83], [62], [123], [78], [79], [124], [52], [53], [80], [63], [125], [107] -Semi-/Self-/Un-Supervised CNN --- [48] [51], [69], [92], [110], [111] [54], [112], [37...…”

Section: Modelmentioning

confidence: 99%

See 3 more Smart Citations

Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark

Cheng,

Wang,

Bao

et al. 2021

Preprint

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Gaze estimation reveals where a person is looking. It is an important clue for understanding human intention. The recent development of deep learning has revolutionized many computer vision tasks, the appearance-based gaze estimation is no exception. However, it lacks a guideline for designing deep learning algorithms for gaze estimation tasks. In this paper, we present a comprehensive review of the appearance-based gaze estimation methods with deep learning. We summarize the processing pipeline and discuss these methods from four perspectives: deep feature extraction, deep neural network architecture design, personal calibration as well as device and platform. Since the data pre-processing and post-processing methods are crucial for gaze estimation, we also survey face/eye detection method, data rectification method, 2D/3D gaze conversion method, and gaze origin conversion method. To fairly compare the performance of various gaze estimation approaches, we characterize all the publicly available gaze estimation datasets and collect the code of typical gaze estimation algorithms. We implement these codes and set up a benchmark of converting the results of different methods into the same evaluation metrics. This paper not only serves as a reference to develop deep learning-based gaze estimation methods but also a guideline for future gaze estimation research. Implemented methods and data processing codes are available at http://phi-ai.org/GazeHub.

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Section: ) Calibration Via Domain Adaptationmentioning

confidence: 99%

Section: ) Calibration Via Domain Adaptationmentioning

confidence: 99%

Section: Featurementioning

confidence: 99%

Section: Modelmentioning

confidence: 99%

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Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark

Cheng,

Wang,

Bao

et al. 2021

Preprint

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“…Unfortunately, the head movements affect the gaze estimation, which makes the approach not mature for real world applications. In [ 93 ], it is introduced a framework that utilizes saliency information in the visual content to transparently adapt the gaze estimation algorithm to the user, without explicit calibration. An algorithm to transform a saliency map into a differentiable loss map that can be used for the optimization of CNN-based models is introduced.…”

Section: Gaze Tracking By Scene Analysismentioning

confidence: 99%

When I Look into Your Eyes: A Survey on Computer Vision Contributions for Human Gaze Estimation and Tracking

Cazzato

Leo

Distante

et al. 2020

Sensors

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The automatic detection of eye positions, their temporal consistency, and their mapping into a line of sight in the real world (to find where a person is looking at) is reported in the scientific literature as gaze tracking. This has become a very hot topic in the field of computer vision during the last decades, with a surprising and continuously growing number of application fields. A very long journey has been made from the first pioneering works, and this continuous search for more accurate solutions process has been further boosted in the last decade when deep neural networks have revolutionized the whole machine learning area, and gaze tracking as well. In this arena, it is being increasingly useful to find guidance through survey/review articles collecting most relevant works and putting clear pros and cons of existing techniques, also by introducing a precise taxonomy. This kind of manuscripts allows researchers and technicians to choose the better way to move towards their application or scientific goals. In the literature, there exist holistic and specifically technological survey documents (even if not updated), but, unfortunately, there is not an overview discussing how the great advancements in computer vision have impacted gaze tracking. Thus, this work represents an attempt to fill this gap, also introducing a wider point of view that brings to a new taxonomy (extending the consolidated ones) by considering gaze tracking as a more exhaustive task that aims at estimating gaze target from different perspectives: from the eye of the beholder (first-person view), from an external camera framing the beholder’s, from a third-person view looking at the scene where the beholder is placed in, and from an external view independent from the beholder.

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