A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms

Kar, Anuradha; Corcoran, Peter

doi:10.1109/access.2017.2735633

Cited by 225 publications

(139 citation statements)

References 185 publications

(168 reference statements)

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“…Kar et al provide a survey of remote gaze estimation techniques [16]. These techniques fall into two main classes depending on whether they are based on infrared light or visible light.…”

Section: Gaze Estimation Methodsmentioning

confidence: 99%

“…Shape based techniques attempt to extract gaze through the use of deformable templates of the eye area to which an eye image is fit [25], [26], [27], [28]. The deformable templates involve a generic representation of an eye, such as two parabolas describing the eye contours and a circle for the iris itself [16]. Then, depending on the image of the eye, the parameters can be adjusted to fit a person's unique eye appropriately.…”

Section: Pccr-based Gaze Estimationmentioning

confidence: 99%

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Are You Really Looking at Me? A Feature-Extraction Framework for Estimating Interpersonal Eye Gaze From Conventional Video

Tran

Sen

Haut

et al. 2022

IEEE Trans. Affective Comput.

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Despite a revolution in the pervasiveness of video cameras in our daily lives, one of the most meaningful forms of nonverbal affective communication, interpersonal eye gaze, i.e. eye gaze relative to a conversation partner, is not available from common video. We introduce the Interpersonal-Calibrating Eye-gaze Encoder (ICE), which automatically extracts interpersonal gaze from video recordings without specialized hardware and without prior knowledge of participant locations. Leveraging the intuition that individuals spend a large portion of a conversation looking at each other enables the ICE dynamic clustering algorithm to extract interpersonal gaze. We validate ICE in both video chat using an objective metric with an infrared gaze tracker (F1=0.846, N=8), as well as in face-to-face communication with expert-rated evaluations of eye contact (r= 0.37, N=170). We then use ICE to analyze behavior in two different, yet important affective communication domains: interrogation-based deception detection, and communication skill assessment in speed dating. We find that honest witnesses break interpersonal gaze contact and look down more often than deceptive witnesses when answering questions (p=0.004, d=0.79). In predicting expert communication skill ratings in speed dating videos, we demonstrate that interpersonal gaze alone has more predictive power than facial expressions.

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“…Kar et al provide a survey of remote gaze estimation techniques [16]. These techniques fall into two main classes depending on whether they are based on infrared light or visible light.…”

Section: Gaze Estimation Methodsmentioning

confidence: 99%

Section: Pccr-based Gaze Estimationmentioning

confidence: 99%

Are You Really Looking at Me? A Feature-Extraction Framework for Estimating Interpersonal Eye Gaze From Conventional Video

Tran

Sen

Haut

et al. 2022

IEEE Trans. Affective Comput.

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show abstract

“…1) Measures of Accuracy for Eye Gaze Tracking Methods: Contemporary research on gaze tracking measures accuracy in a wide variety of ways [9]. For example, commonly used measures include angular resolution in degrees [10], gaze recognition rates in percentage [11], and shifts in number of pixels or distance in cm/mm between gaze [12] and target locations.…”

Section: A Contemporary Methods For Eye Gaze Estimationmentioning

confidence: 99%

Convolutional Neural Network Implementation for Eye-Gaze Estimation on Low-Quality Consumer Imaging Systems

Lemley

Kar

Drîmbarean³

et al. 2019

IEEE Trans. Consumer Electron.

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Accurate and efficient eye gaze estimation is important for emerging consumer electronic systems such as driver monitoring systems and novel user interfaces. Such systems are required to operate reliably in difficult, unconstrained environments with low power consumption and at minimal cost. In this paper a new hardware friendly, convolutional neural network model with minimal computational requirements is introduced and assessed for efficient appearance-based gaze estimation. The model is tested and compared against existing appearance based CNN approaches, achieving better eye gaze accuracy with significantly fewer computational requirements. A brief updated literature review is also provided.

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“…Existing datasets that provide annotations rely on objective frameworks e.g. calculating the direction of gaze or movements of eyes among other techniques (Kar and Corcoran, 2017).…”

Section: Introductionmentioning

confidence: 99%

Subjective Annotations for Vision-based Attention Level Estimation

Coifman¹,

Rohoska

Kristoffersen

et al. 2019

Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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Attention level estimation systems have a high potential in many use cases, such as human-robot interaction, driver modeling and smart home systems, since being able to measure a person's attention level opens the possibility to natural interaction between humans and computers. The topic of estimating a human's visual focus of attention has been actively addressed recently in the field of HCI. However, most of these previous works do not consider attention as a subjective, cognitive attentive state. New research within the field also faces the problem of the lack of annotated datasets regarding attention level in a certain context. The novelty of our work is two-fold: First, we introduce a new annotation framework that tackles the subjective nature of attention level and use it to annotate more than 100,000 images with three attention levels and second, we introduce a novel method to estimate attention levels, relying purely on extracted geometric features from RGB and depth images, and evaluate it with a deep learning fusion framework. The system achieves an overall accuracy of 80.02%. Our framework and attention level annotations are made publicly available.

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A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms

Cited by 225 publications

References 185 publications

Are You Really Looking at Me? A Feature-Extraction Framework for Estimating Interpersonal Eye Gaze From Conventional Video

Are You Really Looking at Me? A Feature-Extraction Framework for Estimating Interpersonal Eye Gaze From Conventional Video

Convolutional Neural Network Implementation for Eye-Gaze Estimation on Low-Quality Consumer Imaging Systems

Subjective Annotations for Vision-based Attention Level Estimation

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